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.
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Urbanowicz, R.J., Bertasius, G., Moore, J.H. (2014). An Extended Michigan-Style Learning Classifier System for Flexible Supervised Learning, Classification, and Data Mining. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_21
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DOI: https://doi.org/10.1007/978-3-319-10762-2_21
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