Abstract
Recently, multi-label classification problem has received significant attention in the research community. This paper is devoted to study the effect of the considered rule heuristic parameters on the generalization error. The results of experiments for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion.
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Alsolami, F., Azad, M., Chikalov, I., Moshkov, M. (2014). Decision Rule Classifiers for Multi-label Decision Tables. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds) Rough Sets and Intelligent Systems Paradigms. Lecture Notes in Computer Science(), vol 8537. Springer, Cham. https://doi.org/10.1007/978-3-319-08729-0_18
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DOI: https://doi.org/10.1007/978-3-319-08729-0_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08728-3
Online ISBN: 978-3-319-08729-0
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