Abstract
Incompleteness is one of the challenging issues in data science. One approach to tackle this issue is using imputation methods to estimate the missing values in incomplete data sets. In spite of the popularity of adopting this approach in several machine learning tasks, it has been rarely investigated in symbolic regression. In this work, a genetic programming (GP) based feature selection and ranking method is proposed and applied to high-dimensional symbolic regression with incomplete data. The main idea is to construct GP programs for each incomplete feature using other features as predictors. The predictors selected by these GP programs are then ranked based on the fitness values of the best constructed GP programs and the frequency of occurrences of the predictors in these programs. The experimental work is conducted on high-dimensional data where the number of features is greater than the number of instances.
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References
Ahmed, S., Zhang, M., Peng, L.: Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming. Conn. Sci. 26(3), 215–243 (2014)
Al-Helali, B., Chen, Q., Xue, B., Zhang, M.: A hybrid GP-KNN imputation for symbolic regression with missing values. In: Mitrovic, T., Xue, B., Li, X. (eds.) AI 2018. LNCS (LNAI), vol. 11320, pp. 345–357. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03991-2_33
Arslan, S., Ozturk, C.: Multi hive artificial bee colony programming for high dimensional symbolic regression with feature selection. Appl. Soft Comput. 78, 515–527 (2019)
Austel, V., et al.: Globally optimal symbolic regression. arXiv preprint arXiv:1710.10720 (2017)
Brandejsky, T.: Model identification from incomplete data set describing state variable subset only-the problem of optimizing and predicting heuristic incorporation into evolutionary system. In: Zelinka, I., Chen, G., Rössler, O., Snasel, V., Abraham, A. (eds.) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol. 210, pp. 181–189. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-319-00542-3_19
Buuren, S.V., Groothuis-Oudshoorn, K.: MICE: multivariate imputation by chained equations in R. J. Stat. Softw. 1–68 (2010)
Chen, Q., Zhang, M., Xue, B.: Feature selection to improve generalization of genetic programming for high-dimensional symbolic regression. IEEE Trans. Evol. Comput. 21(5), 792–806 (2017)
Clarke, R., et al.: The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat. Rev. Cancer 8(1), 37 (2008)
Dick, G.: Bloat and generalisation in symbolic regression. In: Dick, G., et al. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 491–502. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13563-2_42
Donders, A.R.T., Van Der Heijden, G.J., Stijnen, T., Moons, K.G.: A gentle introduction to imputation of missing values. J. Clin. Epidemiol. 59(10), 1087–1091 (2006)
Fortin, F.A., Rainville, F.M.D., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13(Jul), 2171–2175 (2012)
Friedlander, A., Neshatian, K., Zhang, M.: Meta-learning and feature ranking using genetic programming for classification: variable terminal weighting. In: 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 941–948. IEEE (2011)
Koza, J.R.: Genetic Programming II, Automatic Discovery of Reusable Subprograms. MIT Press, Cambridge (1992)
Liu, X., Wang, H., Ye, W., Xing, E.P.: Sparse variable selection on high dimensional heterogeneous data with tree structured responses. arXiv preprint arXiv:1711.08265 (2017)
Muni, D.P., Pal, N.R., Das, J.: Genetic programming for simultaneous feature selection and classifier design (2006)
Neshatian, K., Zhang, M., Andreae, P.: Genetic programming for feature ranking in classification problems. In: Li, X., et al. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 544–554. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89694-4_55
Pires, A., Branco, J.: High dimensionality: the latest challenge to data analysis. arXiv preprint arXiv:1902.04679 (2019)
Pornprasertmanit, S., Miller, P., Schoemann, A., Quick, C., Jorgensen, T., Pornprasertmanit, M.S.: Package ‘simsem’ (2016)
Tran, B.: Evolutionary computation for feature manipulation in classification on high-dimensional data. Ph.D. thesis, Victoria University of Wellington (2018)
Vanschoren, J., Van Rijn, J.N., Bischl, B., Torgo, L.: OpenML: networked science in machine learning. ACM SIGKDD Explor. Newsl. 15(2), 49–60 (2014)
Venkatesh, B., Anuradha, J.: A hybrid feature selection approach for handling a high-dimensional data. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds.) Innovations in Computer Science and Engineering. LNNS, vol. 74, pp. 365–373. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-7082-3_42
Vladislavleva, E., Smits, G., Den Hertog, D.: On the importance of data balancing for symbolic regression. IEEE Trans. Evol. Comput. 14(2), 252–277 (2010)
Xue, B., Zhang, M.: Evolutionary feature manipulation in data mining/big data. ACM SIGEVOlution 10(1), 4–11 (2017)
Zhang, M., Ciesielski, V.: Genetic programming for multiple class object detection. In: Foo, N. (ed.) AI 1999. LNCS (LNAI), vol. 1747, pp. 180–192. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-46695-9_16
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Al-Helali, B., Chen, Q., Xue, B., Zhang, M. (2019). Genetic Programming for Imputation Predictor Selection and Ranking in Symbolic Regression with High-Dimensional Incomplete Data. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_42
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