- author = "Stephan M. Winkler and Michael Affenzeller and Herbert Stekel",
- title = "Evolutionary identification of cancer predictors using clustered data: a case study for breast cancer, melanoma, and cancer in the respiratory system",
- booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion",
- year = "2013",
- editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks",
- isbn13 = "978-1-4503-1964-5",
- keywords = "genetic algorithms, genetic programming",
- pages = "1463--1470",
- month = "6-10 " # jul,
- organisation = "SIGEVO",
- address = "Amsterdam, The Netherlands",
-
DOI = "
doi:10.1145/2464576.2466809",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "In this paper we discuss the effects of using
pre-clustered data on the identification of estimation
models for cancer diagnoses. Based on patients' data
records including standard blood parameters, tumour
markers, and information about the diagnosis of tumors,
the goal is to identify mathematical models for
estimating cancer diagnoses. We have applied a hybrid
clustering and classification approach that first
identifies data clusters (using standard patient data
and tumor markers) and then learns prediction models on
the basis of these data clusters. In the empirical
section we analyse the clusters of patient data samples
formed using k-means clustering: The optimal number of
clusters is identified, and we investigate the
homogeneity of these clusters.
Several evolutionary modelling approaches implemented in HeuristicLab have been applied for subsequently identifying estimators for selected cancer diagnoses: Linear regression, k-nearest neighbour learning, artificial neural networks, and support vector machines (all optimised using evolutionary algorithms) as well as genetic programming. As we show in the results section, the investigated diagnoses of breast cancer, melanoma, and respiratory system cancer can be estimated correctly in up to 84.2percent, 80.3percent, and 94.1percent of the analysed test cases, respectively; without tumour markers up to 78.2percent, 78percent, and 93.3percent of the test samples are correctly estimated, respectively.",
- notes = "Also known as \cite{2466809} Distributed at GECCO-2013.",
