Abstract
In recent years, multi-label classification has attracted a significant body of research, motivated by real-life applications such as text classification and medical diagnoses. However, rule-based methods, and especially Learning Classifier Systems (LCS), for tackling such problems have only been sparsely studied. This is the motivation behind our current work that introduces a generalized multi-label rule format and uses it as a guide for further adapting the general Michigan-style LCS framework. The resulting LCS algorithm is thoroughly evaluated and found competitive to other state-of-the-art multi-label classification methods.
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Allamanis, M., Tzima, F.A., Mitkas, P.A. (2013). Effective Rule-Based Multi-label Classification with Learning Classifier Systems. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_48
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DOI: https://doi.org/10.1007/978-3-642-37213-1_48
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