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Evolutionary model building under streaming data for classification tasks: opportunities and challenges

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Abstract

Streaming data analysis potentially represents a significant shift in emphasis from schemes historically pursued for offline (batch) approaches to the classification task. In particular, a streaming data application implies that: (1) the data itself has no formal ‘start’ or ‘end’; (2) the properties of the process generating the data are non-stationary, thus models that function correctly for some part(s) of a stream may be ineffective elsewhere; (3) constraints on the time to produce a response, potentially implying an anytime operational requirement; and (4) given the prohibitive cost of employing an oracle to label a stream, a finite labelling budget is necessary. The scope of this article is to provide a survey of developments for model building under streaming environments from the perspective of both evolutionary and non-evolutionary frameworks. In doing so, we bring attention to the challenges and opportunities that developing solutions to streaming data classification tasks are likely to face using evolutionary approaches.

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Notes

  1. Multi-step prediction implies that several predictions are made before the true values are known.

  2. A caveat being semi-supervised learning under streaming data, Sect. 5.4.

  3. See model building with embedded versus wrapper or filter frameworks for attribute selection [113].

  4. See also ‘sample selection bias’, Sect. 2.1.

  5. We note that this in itself is a function of assumptions made regarding parameterization. At some point decreasing the size of data chunks will result in performance approaching that of exemplar-wise updating. Observations of this type have informed the use of differing pairwise sliding window durations (Sect. 5.1.1) and evolved temporal features, e.g., [125, 184].

  6. Introns, although non-coding for proteins in biology, appear to describe RNA that play an important role in gene regulation in eukaryotes [35]. In the case of GP, there is generally little or no distinction between genotype and phenotype, and more non-functional code observed than functional code [24, 115].

  7. Younger/older individuals should only be maintained if they were suitably fit.

  8. The majority of datasets employed to date for benchmarking purposes on account of their temporal properties are distributed across multiple repositories (Sect. 4.2.2).

  9. http://meka.sourceforge.net/.

  10. In addition non-overlapping windows have been used, in particular with ensemble methods, with different members of the ensemble being constructed with each new ensemble location (see Sect. 3.2).

  11. Denoting how much of a specific spatio-temporal basis function are present.

  12. For example, as in parameterizing specific technical indicators for feature construction in finance [71].

  13. The decision tree defines the condition under which an action is applied, say, as in sell, buy or hold.

  14. Attempts to cast a multi-class classification problem into at least \(C - 1\) binary classification problems merely emphasizes this effect. Thus, even if the \(C\) classes appear with equal frequency, each binary classification task represents an unequal partition of one class versus the rest.

  15. For a general discussion of this topic (albeit under a non-streaming scenario) see for example Chapter 9 from [59].

  16. Potential short term research goals having been noted in the conclusion (Sect. 6).

References

  1. H.A. Abbass, J. Bacardit, M.V. Butz, X. Llora, Online adaptation in learning classifier systems: stream data mining. Technical report IlliGAL report no. 2004031, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (2004)

  2. H. Abdulsalam, D.B. Skillicorn, P. Martin, Classification using streaming random forests. IEEE Trans. Knowl. Data Eng. 23(1), 22–36 (2012)

    Article  Google Scholar 

  3. A. Agapitos, M. Dyson, J. Kovalchuk, S.M. Lucus. On the genetic programming of time-series predictors for supply chain management, in ACM Genetic and Evolutionary Computation Conference, (2008), pp. 1163–1160

  4. C. Alippi, G. Boracchi, M. Roveri, Just-in-time classifiers for recurrent concepts. IEEE Trans. Neural. Netw. Learn. Syst. 24(4), 620–634 (2013)

    Article  Google Scholar 

  5. F.L. Arcanjo, G.L. Pappa, P.V. Bicalho, W. Meira, A.S. de Silva, Semi-supervised genetic programming for classification, in ACM Genetic and Evolutionary Computation Conference, (2011), pp. 1259–1266

  6. A. Atwater, Towards coevolutionary genetic programming with Pareto archiving under streaming data. Master’s thesis, Faculty of Computer Science (2013)

  7. A. Atwater, M.I. Heywood, Benchmarking Pareto archiving heuristics in the presence of concept drift: diversity versus age, in ACM Genetic and Evolutionary Computation Conference, (2013), pp. 885–892

  8. A. Atwater, M.I. Heywood, A.N. Zincir-Heywood, GP under streaming data constraints: a case for Pareto archiving? in ACM Genetic and Evolutionary Computation Conference, (2012), pp. 703–710

  9. B. Babcock, M. Datar, R. Motwani, Sampling from a moving window over streaming data, in ACM-SIAM Symposium on Discrete Algorithms, (2002), pp. 633–634

  10. K. Bache, M. Lichman, UCI machine learning repository (University of California, Irvine, School of Information and Computer Sciences, 2013), http://archive.ics.uci.edu/ml

  11. K. Badran, P. Rockett, Multi-class pattern classification using single, multi-dimensional feature-space feature extraction evolved by multi-objective genetic programming and its application to network intrusion detection. Genet. Program Evolvable Mach. 13(1), 33–63 (2012)

    Article  Google Scholar 

  12. M. Baena-Garcìa, J. Del Campo-Àvila, R. Fidalgo, A. Bifet, Early drift detection method, in ECML PKDD International Workshop on Knowledge Discovery from Data Streams, (2006) pp. 77–86

  13. M. Behdad, T. French, Online learning classifiers in dynamic environments with incomplete feedback, in IEEE Congress on Evolutionary Computation, (2013), pp. 1786–1793

  14. T.V. Belle, D.H. Ackley, Code factoring and the evolution of evolvability. in Proceedings of the Genetic and Evolutionary Computation Conference, (Morgan Kaufmann, 2002), pp. 1383–1390

  15. U. Bhowan, M. Johnson, M. Zhang, X. Yao, Evolving diverse ensembles using genetic programming for classification with unbalanced data. IEEE Trans. Evol. Comput. 17(3), 368–386 (2013)

    Article  Google Scholar 

  16. U. Bhowan, M. Zhang, M. Johnson, Genetic programming for classification with unbalanced data, in European Conference on Genetic Programming, volume 6021 of LNCS, (2010), pp. 1–12

  17. A. Bifet, Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams, volume 207 of Frontiers in Artificial Intelligence and Applications, (IOS Press, Amsterdam, The Netherlands, 2010)

  18. A. Bifet, E. Frank, G. Holmes, B. Pfahringer, Accurate ensembles for data streams: combining restricted hoeffding trees using stacking, in Proceedings of the Asian Conference on Machine Learning, (2010), pp. 1–16

  19. A. Bifet, R. Gavalda, Learning from time-changing data with adaptive windowing, in SIAM International Conference on Data Mining, (2007), pp. 443–448

  20. A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, R. Gavaldà, New ensemble methods for evolving data streams, in ACM International Conference on Knowledge Discovery and Data Engineering, (2009), pp. 139–148

  21. A. Bifet, I. Žliobaitė, B. Pfahringer, G. Holmes, Pitfalls in benchmarking data stream classification and how to avoid them, in Machine Learning and Knowledge Discovery in Databases, volume 8188 of LNCS, (2013), pp. 465–479

  22. T. Blackwell, J. Branke, Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans. Evol. Comput. 10(4), 459–472 (2006)

    Article  Google Scholar 

  23. D. Brain, G.I. Webb, The need for low bias algorithms in classification learning from large data sets, in Principles of Knowledge Discovery and Datamining, volume 2431 of LNCS, (2002), pp. 62–73

  24. M. Brameier, W. Banzhaf, Linear Genetic Programming (Springer, Berlin, 2007)

    MATH  Google Scholar 

  25. J. Branke, E. Salihoğlu, Ş. Uyar, Towards an analysis of dynamic environments, in Proceedings of the ACM Genetic and Evolutionary Computation Conference, (2005), pp. 1433–1440

  26. G. Brown, L.I. Kuncheva, “Good” and “bad” diversity in majority vote ensembles, in Multiple Classifier Systems, volume 5997 of LNCS, (2010), pp. 124–133

  27. D. Brzezinski, J. Stefanowski, Reacting to different types of concept drift: the accuracy updated ensemble algorithm. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 81–94 (2014)

    Article  Google Scholar 

  28. E.K. Burke, S. Gustafson, G. Kendall, Diversity in genetic programming: an analysis of measures and correlation with fitness. IEEE Trans. Evol. Comput. 8(1), 47–62 (2004)

    Article  Google Scholar 

  29. M. Butler, D. Kazakov, A learning adaptive Bollinger band system, in IEEE Conference on Computational Intelligence on Financial Engineering and Economics, (2012), pp. 1–8

  30. R. Calabretta, S. Nolfi, D. Parisi, G.P. Wagner, Duplication of modules facilitates the evolution of functional specialization. Artif. Life 6(1), 69–84 (2000)

    Article  Google Scholar 

  31. E. Carreño Jara, Long memory time series forecasting by using genetic programming. Genet. Program Evolvable Mach. 12(3), 429–456 (2011)

    Article  Google Scholar 

  32. A. Cervantes, P. Isasi, C. Gagné, M. Parizeau, Learning from non-stationary data using a growing network of prototypes, in IEEE Congress on Evolutionary Computation, (2013), pp. 2634–2641

  33. O. Chapelle, B. Scholkopf, A. Zien, Semi-Supervised Learning (MIT Press, Cambridge, MA, 2006)

  34. S. Chen, H. He, Towards incremental learning of non-stationary imbalanced data stream: a multiple selectively recursive approach. Evol. Syst. 2(1), 35–50 (2011)

    Article  Google Scholar 

  35. M. Chorev, L. Carmel, The function of introns. Front. Genet. 3(55) (2012). doi:10.3389/fgene.2012.00055

  36. J. Clune, J.-B. Mouret, H. Lipson, The evolutionary origins of modularity. Proc. R. Soc. B Biol. Sci. 280(20122863), 1–9 (2013)

    Google Scholar 

  37. H.G. Cobb, An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent non-stationary environments. Technical report AIC-90-001, Naval Research Laboratory, Washington, USA, (1990)

  38. L. Cohen, G. Avrahami-Bakish, M. Last, A. Kandel, O. Kipersztok, Real-time data mining of non-stationary data streams from sensor networks. Inf. Fusion 9(3), 344–353 (2008)

    Article  Google Scholar 

  39. D. Cohn, L. Atlas, R. Ladner, Improving generalization with active learning. Mach. Learn. 15(2), 201–221 (1994)

    Google Scholar 

  40. K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, Y. Singer, Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551–585 (2006)

    MathSciNet  MATH  Google Scholar 

  41. R. Curry, M. I. Heywood, One-class genetic programming, in European Conference on Genetic Programming, volume 5481 of LNCS, (2009), pp. 1–12

  42. R. Curry, P. Lichodzijewski, M.I. Heywood, Scaling genetic programming to large datasets using hierarchical dynamic subset selection. IEEE Trans. Syst. Man Cybern. B 37(4), 1065–1073 (2007)

    Article  Google Scholar 

  43. H.H. Dam, C. Lokan, H.A. Abbass, Evolutionary online data mining: an investigation in a dynamic environment, in Studies in Computational Intelligence, vol 51, chapter 7, (Springer, 2007), pp. 153–178

  44. T. Dasu, S. Krishnan, S. Venkatasubramanian, K. Yi, An information-theoretic approach to detecting changes in multi-dimensional data streams, in Proceedings of the Symposium on the Interface of Statistics, (2006)

  45. M. Datar, A. Gionis, P. Indyk, R. Motwani, Maintaining stream statistics over sliding windows, in ACM-SIAM Symposium on Discrete Algorithms, (2002), pp. 635–644

  46. A.P. Dawid, Statistical theory: the prequential approach. J. R. Stat. Soci. A 147, 278–292 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  47. E.D. de Jong, A monotonic archive for pareto-coevolution. Evol. Comput. 15(1), 61–94 (2007)

    Article  Google Scholar 

  48. K. A. de Jong, Evolving in a changing world, in Proceedings of the International Symposium on Foundations of Intelligent Systems, (Springer, 1999), pp. 512–519

  49. I. Dempsey, M. O’Neill, A. Brabazon, Adaptive trading with grammatical evolution, in IEEE Congress on Evolutionary Computation, (2006), pp. 2587–2592

  50. I. Dempsey, M. O’Neill, A. Brabazon, Foundations in Grammatical Evolution for Dynamic Environments, volume 194 of Studies in Computational Intelligence (Springer, 2009)

  51. I. Dempsey, M. O’Neill, A. Brabazon, Survey of EC in dynamic environments, chapter 3, (2009), pp. 25–54. In [50]

  52. M.A.H. Dempster, C.M. Jones, A real-time adaptive trading system using genetic programming. Quant. Financ. 1, 397–413 (2001)

    Article  Google Scholar 

  53. G. Ditzler, R. Polikar, Hellinger distance based drift detection for non-stationary environments, in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, (2011), pp. 41–48

  54. G. Ditzler, R. Polikar, Semi-supervised learning in non-stationary environments, in IEEE-INNS International Joint Conference on Neural Networks, (2011), pp. 1–8

  55. G. Ditzler, R. Polikar, Incremental learning of concept drift from streaming imbalanced data. IEEE Trans. Knowl. Data Eng. 25(10), 2283–2301 (2013)

    Article  Google Scholar 

  56. G. Ditzler, G. Rosen, R. Polikar, Discounted expert weighting for concept drift, in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, (2013), pp. 61–66

  57. P. Domingos, G. Hulten, Catching up with the data: research issues in mining data streams, in Workshop on Research Issues in Data Mining and Knowledge Discovery, (2001)

  58. J. Doucette, M. I. Heywood, GP classification under imbalanced data sets: active sub-sampling AUC approximation, in European Conference on Genetic Programming, volume 4971 of LNCS, (2008)

  59. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd edn. (Wiley, New York, 2001)

  60. K. Dyer, R. Capo, R. Polikar, COMPOSE: a semi-supervised learning framework for initially labeled non-stationary streaming data. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 12–26 (2014)

    Article  Google Scholar 

  61. M. Ebner, M. Shackleton, R. Shipman, How neutral networks influence evolvability. Complexity 7(2), 19–33 (2002)

    Article  MathSciNet  Google Scholar 

  62. J. Eggermont, A.E. Eiben, J.I. van Hemert, Adapting the fitness function in GP for data mining, in European Conference on Genetic Programming, volume 1598 of LNCS, (1999), pp. 195–204

  63. J. Eggermont, T. Lenaerts, S. Poyhonen, A. Termier, Raising the dead: extending evolutionary algorithms with a case-based memory, in European Conference on Genetic Programming, volume 2038 of LNCS, (2001), pp. 280–290

  64. A. Ekárt, S. Németh, Maintaining the diversity of genetic programming, in European Conference on Genetic Programming, volume 2278 of LNCS, (2002), pp. 162–171

  65. R. Elwell, R. Polikar, Incremental learning of concept drift in non-stationary environments. IEEE Trans. Neural Netw. 22(10), 1517–1531 (2011)

    Article  Google Scholar 

  66. S. Esmeir, S. Markovitch, Anytime learning of any cost classifiers. Mach. Learn. 82, 445–473 (2011)

    Article  MathSciNet  Google Scholar 

  67. C. Espinosa-Soto, A. Wagner, Specialization can drive the evolution of modularity. PLoS Comput. Biol. 6, e1000719:1–10, (2010)

  68. W. Fan, Y. Huang, H. Wang, P.S. Yu, Active mining of data streams, in Proceedings of SIAM International Conference on Data Mining, (2004), pp. 457–461

  69. T. Fawcett, “In vivo” spam filtering: a challenge problem for KDD. ACM SIGKDD Explor. 5(2), 140–198 (2003)

    Article  Google Scholar 

  70. A. Fern, R. Givan, Online ensemble learning: an empirical study. Mach. Learn. 53, 71–109 (2003)

    Article  MATH  Google Scholar 

  71. P. Fernandez-Blanco, D. Bosdas-Sego, F. Soltero, J.I. Hidalgo, Technical market indicators optimization using evolutionary algorithms, in ACM Genetic and Evolutionary Computation Conference—ARC-FEC Workshop, (2008), pp. 1851–1858

  72. S.G. Ficici, J.B. Pollack, Pareto optimality in coevolutionary learning, in European Conference on Artificial Life, (2001), pp. 286–297

  73. G. Folino, G. Papuzzo, Handling different categories of concept drift in data streams using distributed GP, in European Conference on Genetic Programming, volume 6021 of LNCS, (2010), pp. 74–85

  74. G. Folino, C. Pizzuti, G. Spezzano, Training distributed GP ensemble with a selection algorithm based on clustering and pruning for pattern classification. IEEE Trans. Evol. Comput. 12(4), 458–468 (2008)

    Article  Google Scholar 

  75. Y. Freund, R. Shapire, A decision-theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)

    Article  MATH  Google Scholar 

  76. J. Gama, Knowledge Discovery from Data Streams (CRC Press, Boca Raton, 2010)

    Book  MATH  Google Scholar 

  77. J. Gama, A survey on learning from data streams: current and future trends. Prog. Artif. Intell. 1(1), 45–55 (2012)

    Article  Google Scholar 

  78. J. Gama, P. Medas, G. Castillo, P.P. Rodrigues, Learning with drift detection, in Advances in Artificial Intelligence, volume 3171 of LNCS, (2004), pp. 66–112

  79. J. Gama, R. Sebastião, P. Rodrigues, On evaluating stream learning algorithms. Mach. Learn. 90(3), 317–346 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  80. J. Gama, R. Sebastiao, P.P. Rodrigues, Issues in evaluation of stream learning algorithms, in ACM Conference on Knowledge Discovery and Data Mining, (2009), pp. 329–338

  81. J. Gao, W. Fan, J. Han, On appropriate assumptions to mine data streams: analysis and practice, in IEEE International Conference on Data Mining, (2007), pp. 143–152

  82. J.W. Gao, W. Fan, J. Han, P.S. Yu, A general framework for mining concept-drifting data streams with skewed distributions, in Proceedings of SIAM International Conference on Data Mining, (2007), pp. 3–14

  83. C. Gathercole, P. Ross, Dynamic training subset selection for supervised learning in genetic programming, in Parallel Problem Solving Nature, volume 866 of LNCS, (1994), pp. 312–321

  84. A. Ghazikhani, R. Monsefi, H.S. Yazdi, Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams. Neural Comput. Appl. 23, 1283–1295 (2013)

    Article  Google Scholar 

  85. A. Ghosh, S. Tstutsui, H.Tanaka, Function optimization in non-stationary environment using steady state genetic algorithms with aging of individuals, in IEEE Conference on Evolutionary Computation, (1998), pp. 666–671

  86. A. Godase, V. Attar, Classification of data streams with skewed distributions, in IEEE Workshop on Evolving and Adaptive Intelligent Systems, (2013), pp. 151–156

  87. J.J. Greffenstette, Genetic algorithms for changing environments, in Proceedings of Parallel Problem Solving from Nature, volume 2, (Elsevier, 1992), pp. 137–144

  88. S. Grossberg, Nonlinear neural networks: principles, mechanisms, and architectures. Neural Netw. 1(2), 17–61 (1988)

    Article  Google Scholar 

  89. M. Harries, Splice-2 comparative evaluation: electricity pricing. Technical report, University of New South Wales (1999)

  90. H. He, S. Chen, IMORL: incremental multiple-object recognition and localization. IEEE Trans. Neural Netw. 19(10), 1727–1738 (2008)

    Article  Google Scholar 

  91. H. He, E.A. Garcia, Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)

    Article  Google Scholar 

  92. R.C. Holt, Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11, 63–91 (1993)

    Article  MathSciNet  Google Scholar 

  93. G.S. Hornby, ALPS: the age layered population structure for reducing the problem of premature convergence, in ACM Genetic and Evolutionary Computation Conference, (2006), pp. 815–822

  94. T. Hu, W. Banzhaf, Neutrality and variability: two sides of evolvability in linear genetic programming, in ACM Genetic and Evolutionary Computation Conference, (2009) pp. 963–970

  95. T. Hu, W. Banzhaf, The role of population size in rate of evolution in genetic programming, in European Conference on Genetic Programming, volume 5481 of LNCS, (2009), pp. 85–96

  96. T. Hu, W. Banzhaf, Evolvability and speed of evolutionary algorithms in light of recent developments in biology. J. Artif. Evol. Appl. 2010:568375, 1–28, (2010)

  97. S. Huang, Y. Dong, An active learning system for mining time changing data streams. Intell. Data Anal. 11(4), 401–419 (2007)

    Google Scholar 

  98. L. Huelsbergen, Finding general solutions to the parity problem by evolving machine-language representations, in European Conference on Genetic Programming, (Morgan Kaufmann, 1998), pp. 158–166

  99. E. Ikonomovska. DataExpo: Airline dataset, (2009)

  100. K. Imamura, T. Soule, R.B. Heckendorn, J.A. Foster, Behavioral diversity and a probabilistically optimal GP ensemble. Genet. Program Evolvable Mach. 4(3), 235–254 (2003)

    Article  Google Scholar 

  101. N. Japkowicz, M. Shah, Evaluating Learning Algorithms: A classification perspective (Cambridge University Press, Cambridge, 2012)

    Google Scholar 

  102. M. Karnick, M.D. Muhlbaier, R. Polikar, Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach, in Proceedings of the International Conference on Pattern Recognition, (2008), pp. 1–4

  103. N. Kashtan, E. Noor, U. Alon, Varying environments can speed up evolution. Proc. Nat. Acad. Sci. 104(34), 13713–13716 (2007)

    Article  Google Scholar 

  104. A. Kattan, A. Agapitos, R. Poli, Unsupervised problem decomposition using genetic programming, in Proceedings of the European Conference on Genetic Programming, volume 6021 of LNCS, (2010) pp. 122–133

  105. D. Kifer, S. Ben-David, J. Gehrke, Detecting change in data streams, in Proceedings of the International Conference on Very Large Data Bases, (Morgan Kaufmann, 2004), pp. 180–191

  106. R. Klinkenberg, I. Renz, Adaptive information filtering: learning in the presence of concept drifts, in ICML/AAAI Workshop on Learning for Text Categorization, (AAAI, 1998), pp. 33–40

  107. J.Z. Kolter, M.A. Maloof, Dynamic weighted majority: an ensemble method for drifting concepts. J. Mach. Learn. 8, 2755–2790 (2007)

    MATH  Google Scholar 

  108. M.F. Korns, Symbolic regression of conditional target expressions, in Genetic Programming Theory and Practice VII, eds. by R. Riolo, U.-M. O’Reilly, T. McConaghy, chapter 13, (Springer, 2010), pp. 211–228

  109. T. Kovacs, Strength or Accuracy: Credit Assignment in Learning Classifier Systems (Springer, Berlin, 2004)

    Book  Google Scholar 

  110. K. Krawiec, Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genet. Program Evolvable Mach. 3(4), 329–343 (2002)

    Article  MATH  Google Scholar 

  111. H.-P. Kriegel, P. Kröger, A. Zimek, Subspace clustering. WIREs Data Min Knowl. Discov. 2, 351–364 (2012)

    Article  Google Scholar 

  112. L.I. Kuncheva, Classifier ensembles for changing environments, in Multiple Classifier Systems, volume 3077 of LNCS, (2004), pp. 1–15

  113. T.N. Lal, O. Chapelle, J. Weston, A. Elisseeff, Embedded methods, in Feature Extraction: Foundations and Applications, volume 207 of Studies in Fuzziness and Soft Computing, chapter 5, (Springer, 2006), pp. 137–165

  114. W.B. Langdon, B.F. Buxton, Evolving receiver operating characteristics for data fusion, in Proceedings of the European Conference on Genetic Programming, volume 2038 of LNCS, (2001), pp. 87–96

  115. W.B. Langdon, R. Poli, Foundations of Genetic Programming (Springer, Berlin, 2001)

    Google Scholar 

  116. T. Lange, A. Rahbek, An introduction to regime switching time series models, in Handbook of Financial Time Series, eds. by T.G. Anderson, R.A. Davis, J.P. Kreiß, T.V. Mikosch, (Spriner, 2009), pp. 871–887

  117. C. Lanquillon, Information filtering in changing domains, in Proceedings of the International Joint Conference on Artificial Intelligence, (1999), pp. 41–48

  118. D. Lewis, Evaluating and optimizing autonomous text classification systems, in ACM International Conference on Research and Development in Information Retrieval, (1995), pp. 246–254

  119. D. Lewis, Y. Yang, T. Rose, F. Li, Rcv1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)

    Google Scholar 

  120. J. Lewis, E. Hart, G. Ritchie, A comparison of dominance mechanisms and simple mutation on non-stationary problems, in Parallel Problem Solving from Nature, volume 1498 of LNCS, (1998), pp. 139–148

  121. P. Li, X. Wu, X. Hu, Mining recurring concept drifts with limited labeled streaming data. ACM Trans. Intell. Syst. Technol. 3(2), 29:1–29:32 (2012)

  122. P. Lichodzijewski, M.I. Heywood, Managing team-based problem solving with symbiotic bid-based genetic programming, in ACM Genetic and Evolutionary Computation Conference, (2008), pp. 363–370

  123. P. Lindstrom, B. MacNamee, S.J. Delany, Handling concept drift in a text data stream constrained by high labelling cost, in Proceedings of the International Florida Artificial Intelligence Research Society Conference, ( AAAI, 2010)

  124. P. Lindstrom, B. MacNamee, S.J. Delany, Drift detection using uncertainty distribution divergence. Evol. Intel. 4(1), 13–25 (2013)

    Google Scholar 

  125. A. Loginov, M.I. Heywood, On the impact of streaming interface heuristics on GP trading agents: an FX benchmarking study, in Proceedings of the ACM Genetic and Evolutionary Computation Conference, (2013), pp. 1341–1348

  126. A. Loginov, M.I. Heywood, On evolving multi-agent FX traders, in EvoApplications, volume 8602 of LNCS, (2014)

  127. E. Lughofer, On-line active learning based on enhanced reliability concepts, in IEEE Workshop on Evolving and Adaptive Intelligent Systems, (2013), pp. 1–6

  128. S. Ma, C. Ji, Performance and efficiency: recent advances in supervised learning. Proc. IEEE 87(9), 1519–1536 (1999)

    Article  Google Scholar 

  129. M. Markou, S. Singh, Novelty detection: a review-part 1: statistical approaches. Sig. Process. 83, 2481–2497 (2003)

    Article  MATH  Google Scholar 

  130. M. Markou, S. Singh, Novelty detection: a review-part 2: neural network based approaches. Sig. Process. 83, 2499–2521 (2003)

    Article  MATH  Google Scholar 

  131. A.R. McIntyre, M.I. Heywood, Cooperative problem decomposition in Pareto competitive classifier models of coevolution, in European Conference on Genetic Programming, volume 4971 of LNCS, (2008), pp. 289–300

  132. A.R. McIntyre, M.I. Heywood, Pareto cooperative-competitive genetic programming: a classification benchmarking study, in Genetic Programming Theory and Practice, eds. by R. Riolo, T. Soule, B. Worzel, volume IV, chapter 4, (Springer, 2008), pp. 43–60

  133. A.R. McIntyre, M.I. Heywood, Classification as clustering: a pareto cooperative-competitive GP approach. Evol. Comput. 19(1), 137–166 (2011)

    Article  Google Scholar 

  134. J.H. Metzen, M. Edgington, Y. Kassahun, F. Kirchner, Analysis of an evolutionary reinforcement learning method in a multiagent domain, in Proceedings of the ACM International Joint Conference on Autonomous Agents and Multiagent Systems, (2008), pp. 291–298

  135. L.L. Minku, Concept drift datasets and generators (2010), http://www.cs.bham.ac.uk/~minkull/opensource.html

  136. L.L. Minku, H. Inoue, X. Yao, Negative correlation in incremental learning. Nat. Comput. J. 8, 289–320 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  137. L.L. Minku, A.P. White, X. Yao, The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Trans. Knowl. Data Eng. 22(5), 730–742 (2010)

    Article  Google Scholar 

  138. L.L. Minku, X. Yao, DDD: a new ensemble approach for dealing with concept drift. IEEE Trans. Knowl. Data Eng. 24(4), 619–633 (2012)

    Article  Google Scholar 

  139. N. Mori, H. Kita, Y. Nishikawa, Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm, in Parallel Problem Solving from Nature, volume 1498 of LNCS, (1998), pp. 149–157

  140. R.W. Morrison, Designing Evolutionary Algorithms for Dynamic Environments. Natural Computing (Springer, Berlin, 2004)

  141. Neurotech. Pakdd 2009 data mining competition, (2009)

  142. H.M. Nguyen, E.W. Cooper, K. Kamei, Online learning from imbalanced data streams, in International Conference on Soft Computing and Pattern Recognition, (2011), pp. 347–352

  143. N. Nikolaev, H. Iba, Accelerated genetic programming of polynomials. Genet. Program Evolvable Mach. 2(3), 231–257 (2000)

    Article  Google Scholar 

  144. K. Nishida, K. Yamauchi, Learning, detecting, understanding, and predicting concept changes, in IEEE-INNS International Joint Conference on Neural Networks, (2009), pp. 2280–2287

  145. J. Noble, R. Watson, Pareto coevolution: using performance against coevolved opponents in a game as dimensions for pareto selection, in Genetic and Evolutionary Computation Conference, (Morgan Kaufmann, 2001), pp. 493–500

  146. M. O’Neill, C. Ryan, Grammatical evolution by grammatical evolution: the evolution of grammar and genetic code, in European Conference on Genetic Programming, volume 3003 of LNCS, (2004), pp. 138–149

  147. N.C. Oza, S. Russell, Experimental comparison of online and batch versions of bagging and boosting, in ACM International Conference on Knowledge Discovery and Data Mining, (2001), pp. 359–364

  148. G.L. Pappa, G. Ochoa, M.R. Hyde, A.A. Freitas, J. Woodward, J. Swan, Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms. Genet. Program Evolvable Mach. 15(1), 3–35 (2014)

    Article  Google Scholar 

  149. M. Parter, N. Kashtan, U. Alon, Facilitated variation: How evolution learns from past environments to generalize to new environments. PLoS Comput. Biol. 4(11), e1000206 (2008)

    Article  Google Scholar 

  150. A. Pocock, P. Yiapanis, J. Singer, M. Luján, G. Brown, Online non-stationary boosting, in Multiple Classifier Systems, volume 5997 of LNCS, (2010), pp. 205–214

  151. R. Polikar, R. Elwell, Benchmark datasets for evaluating concept drift/nse algorithms (2011), http://users.rowan.edu/?polikar/research/NSE

  152. R. Polikar, L. Udpa, S.S. Udpa, V. Honavar, Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans. Syst. Man Cybern. C 31(4), 497–508 (2001)

    Article  Google Scholar 

  153. A. Prugel-Bennett, Benefits of a population: five mechanisms that advantage population-based algorithms. IEEE Trans. Evol. Comput. 14(4), 500–517 (2010)

    Article  Google Scholar 

  154. J. Quinonero-Candela, M. Sugiyama, A. Schwaighofer, N.D. Lawrence, (eds.), Dataset Shift in Machine Learning (MIT Press, 2009)

  155. S. Rahimi, A.R. McIntyre, M.I. Heywood, N. Zincir-Heywood, Label free change detection on streaming data with cooperative multi-objective genetic programming, in ACM Genetic and Evolutionary Computation Conference, (2013), pp. 159–160

  156. K. Rodríguez-Vázquez, P.J. Fleming, Evolution of mathematical models of chaotic systems based on multi objective genetic programming. Knowl. Inf. Syst. 8(2), 235–256 (2005)

    Article  Google Scholar 

  157. R. Schapire, Y. Freund, Decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  158. M. Scholz, R. Klinkenberg, Boosting classifiers for drifting concepts. Intell. Data Anal. 11(1), 3–28 (2007)

    Google Scholar 

  159. R. Schwaerzel, T. Bylander, Predicting currency exchange rates by genetic programming with trigonometric functions and high-order statistics, in ACM Genetic and Evolutionary Computation Conference, (2006), pp. 955–956

  160. R. Sebastio, J. Gama, Change detection in learning histograms from data streams, in Proceedings of the Portuguese Conference on Artificial Intelligence, volume 4874 of LNCS, (Springer, 2007), pp. 112–123

  161. H.A. Simon, The architecture of complexity. Proc. Am. Philos. Soc. 106, 467–482 (1962)

    Google Scholar 

  162. P. Sobolewski, M. Wozniak, LDCnet: minimizing the cost of supervision for various types of concept drift, in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, (2013), pp. 68–75

  163. D. Song, M.I. Heywood, A.N. Zincir-Heywood, Training genetic programming on half a million patterns: an example from anomaly detection. IEEE Trans. Evol. Comput. 9(3), 225–239 (2005)

    Article  Google Scholar 

  164. K.O. Stanley, R. Miikkulainen, Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)

    Article  Google Scholar 

  165. R. Stapenhurst, G. Brown, Theoretical and empirical analysis of diversity in non-stationary learning. in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (2011), pp. 25–32

  166. A. Storkey, When training and test sets are different: characterizing learning transfer, chapter 1, (2009), pp. 3–28. In [156]

  167. W. Street, Y. Kim, A streaming ensemble algorithm (SEA) for large-scale classification, in ACM Conference on Knowledge Discovery and Data Mining, (2001), pp. 377–382

  168. R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction (MIT Press, Cambridge, 1998)

    Google Scholar 

  169. R. Swan, J. Allan, Extracting significant time varying features from text, in ACM International Conference on Information and Knowledge Management, (1999), pp. 38–45

  170. K. Trojanowski, Z. Michalewicz, Evolutionary optimization in non-stationary environments. J. Comput. Sci. Technol. 1(2), 93–124 (2000)

    Google Scholar 

  171. A. Tsymbal, M. Pechenizkiy, P. Cunningham, S. Puuronen, Dynamic integration of classifiers for handling concept drift. Inf. Fusion 9(1), 56–68 (2008)

    Article  Google Scholar 

  172. P.D. Turney, Increasing evolvability considered as a large-scale trend in evolution, in Genetic and Evolutionary Computation Conference: Workshop on Evolvability, (Morgan Kaufmann, 1999), pp. 43–46

  173. U.S. National Oceanic and Atmospheric Administration. Federal climate complex global surface summary of day data (2010), ftp://ftp.ncdc.noaa.gov/pub/data/gsod

  174. A.Ş. Uyar, A.E. Harmanci, Performance comparisons of genotype-to-phenotype mapping schemes for diploid representations in changing environments, in International Conference on Recent Advances in Soft Computing, (2002), pp. 128–134

  175. A. Vahdat, A. Atwater, A.R. McIntyre, M.I. Heywood, On the application of GP to streaming data classification tasks with label budgets, in ACM Genetic and Evolutionary Computation Conference: ECBDL Workshop, (2014), pp. 1287–1294

  176. A. Vahdat, J. Morgan, A.R. McIntyre, M.I. Heywood, A.N. Zincir-Heywood, Evolving GP classifiers for streaming data tasks with concept change and label budgets: a benchmarking study, in Handbook of Genetic Programming Applications (Springer, under review)

  177. H. Valizadegan, P.-N. Tan, A prototype-driven framework for change detection in data stream classification, in IEEE Symposium on Computational Intelligence and Data Mining, (2007), pp. 88–95

  178. L. Vanneschi, G. Cuccu, Variable size population for dynamic optimization with genetic programming, in ACM Genetic and Evolutionary Computation Conference, (2009), pp. 1895–1896

  179. W. Verbeke, K. Dejager, D. Martens, J. Nur, B. Basens, New insights into churn prediction in the telecommunication sector: a profit driven data mining approach. Eur. J. Oper. Res. 218, 211–229 (2012)

    Article  Google Scholar 

  180. E. Vladislavleva, G. Smits, D. den Hertog, On the importance of data balancing for symbolic regression. IEEE Trans. Evol. Comput. 14(2), 252–277 (2010)

    Article  Google Scholar 

  181. P. Vorburger, A. Bernstein, Entropy-based concept shift detection, in Proceedings of the Sixth International Conference on Data Mining, (2006), pp. 1113–1118

  182. A. Wagner, Environmental change in adaptation and innovation, in The Origins of Evolutionary Innovations, chapter 11 (Oxford University Press, 2011)

  183. G.P. Wagner, L. Altenberg, Complex adaptations and the evolution of evolvability. Complexity 50(3), 433–452 (1996)

    Google Scholar 

  184. N. Wagner, Z. Michalewicz, M. Khouja, R.R. McGregor, Time series forecasting for dynamic environments: the DyFor genetic program model. IEEE Trans. Evol. Comput. 11(4), 433–452 (2007)

    Article  Google Scholar 

  185. J. Wang, P. Zhao, S.C.H. Hoi, R. Jin, Online feature selection and its applications. IEEE Trans. Knowl. Data Eng. 26(3), 698–710 (2014)

    Article  Google Scholar 

  186. S. Wang, L.L. Minku, X. Yao, A learning framework for online class imbalance learning, in IEEE Symposium on Computational Intelligence and Ensemble Learning (2013), pp. 36–45

  187. Y. Wang, M. Wineberg, Estimation of evolvability genetic algorithm and dynamic environments. Genet. Program Evolvable Mach. 7(3), 355–382 (2006)

    Article  Google Scholar 

  188. R.A. Watson, J.B. Pollack, Modular interdependency in complex dynamic systems. Artif. Life 11(4), 445–457 (2005)

    Article  Google Scholar 

  189. G.M. Weiss, R. Provost, Learning when training data are costly: the effect of class distribution on tree induction. J. Artif. Intell. Res. 19, 315–354 (2003)

    MATH  Google Scholar 

  190. G. Widmer, M. Kubat, Effective learning in dynamic environments by explicit context tracking, in Proceedings of the European Conference on Machine Learning, volume 667 of LNCS, (1993), pp. 227–243

  191. G. Wilson, W. Banzhaf, Interday and intraday stock trading using PAM developmental GP and linear GP, in Natural Computing in Computational Finance 3, volume 293 of SCI, chapter 11, eds. by A. Brabazon, M. ONeill, D.G. Maringer, (Springer, 2010), pp. 191–212

  192. X. Wu, K. Yu, W. Ding, H. Wang, X. Zhu, Online feature selection with streaming features. IEEE Trans. Pattern Anal. Mach. Learn. 35(5), 1178–1182 (2013)

    Article  Google Scholar 

  193. Y. Xu, S. Furao, O. Hasegawa, J. Zhao, An online incremental learning vector quantization, in Advances in Knowledge Discovery and Data Mining, volume 5476 of LNAI, (2009), pp. 1046–1053

  194. S. Yang, Dominance learning in diploid genetic algorithms for dynamic optimization problems, in ACM Genetic and Evolutionary Computation Conference, (2006), pp. 1435–1448

  195. Y. Yang, X. Wu, X. Zhu, Mining in anticipation for concept change: proactive-reactive prediction in data streams. Data Min. Knowl. Disc. 13(3), 261–289 (2006)

    Article  MathSciNet  Google Scholar 

  196. M. Zhang, W. Smart, Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification. Pattern Recogn. Lett. 27(11), 1266–1274 (2006)

    Article  Google Scholar 

  197. P. Zhang, X. Zhu, L. Guo, Mining data streams with labeled and unlabeled training examples, in IEEE International Conference on Data Mining, (2009), pp. 627–636

  198. P. Zhang, X. Zhu, J. Tan, L. Guo, Classifier and cluster ensembles for mining concept drifting data streams, in IEEE International Conference on Data Mining, (2010), pp. 1175–1180

  199. X. Zhu, P. Zhang, X. Lin, Y. Shi, Active learning from stream data using optimal weight classifier ensemble. IEEE Trans. Syst. Man Cybern. B 40(6), 1607–1621 (2010)

    Article  Google Scholar 

  200. T. Ziemke, N. Bergfeldt, G. Buason, T. Susi, H. Svensson, Evolving cognitive scaffolding and environment adaptation: a new research direction for evolutionary robotics. Connect. Sci. 16(4), 339–350 (2004)

    Article  Google Scholar 

  201. I. Žliobaitė, Change with delayed labelling: When is it detectable? in IEEE International Conference on Data Mining Workshops, (2010), pp. 843–850

  202. I. Žliobaitė, A. Bifet, B. Pfahringer, G. Holmes, Active learning with evolving streaming data, in Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, (Springer, 2011), pp. 597–612

  203. I. Žliobaitė, A. Bifet, B. Pfahringer, G. Holmes, Active learning with drifting streaming data. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 27–54, (2014)

  204. I. Žliobaitė, B. Gabrys, Adaptive preprocessing for streaming data. IEEE Trans. Knowl. Data Eng. 26(2), 309–321 (2014)

    Article  Google Scholar 

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Acknowledgments

The author would like to thank the reviewers for their constructive feedback on this article resulting in significant improvements on earlier drafts. Support through the NSERC CRD Grant program and RUAG Schweiz AG is readily acknowledged.

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Heywood, M.I. Evolutionary model building under streaming data for classification tasks: opportunities and challenges. Genet Program Evolvable Mach 16, 283–326 (2015). https://doi.org/10.1007/s10710-014-9236-y

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