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An Extended Michigan-Style Learning Classifier System for Flexible Supervised Learning, Classification, and Data Mining
Ryan J. Urbanowicz, Gediminas Bertasius, and Jason H. Moore
Institute for Quantitative Biomedical Sciences, Department of Genetics Geisel School of Medicine, Lebanon, NH, USAryan.j.urbanowicz@dartmouth.edu
jason.h.moore@dartmouth.edu
http://www.epistasis.org/
Abstract. Advancements in learning classifier system (LCS) algorithms have highlighted their unique potential for tackling complex, noisy problems, as found in bioinformatics. Ongoing research in this domain must address the challenges of modeling complex patterns of association, systems biology (i.e. the integration of different data types to achieve a more holistic perspective), and ‘big data’ (i.e. scalability in large-scale analysis). With this in mind, we introduce ExSTraCS (Extended Supervised Tracking and Classifying System), as a promising platform to address these challenges using supervised learning and a Michigan-Style LCS architecture. ExSTraCS integrates several successful LCS advancements including attribute tracking/feedback, expert knowledge covering (with four built-in attribute weighting algorithms), a flexible and efficient rule representation (handling datasets with both discrete and continuous attributes), and rapid non-destructive rule compaction. A few novel mechanisms, such as adaptive data management, have been included to enhance ease of use, flexibility, performance, and provide groundwork for ongoing development.
Keywords: Learning Classifier System, Genetics, Epidemiology, Epistasis, Heterogeneity, Evolutionary Algorithm, Systems Biology
LNCS 8672, p. 211 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014