Abstract
This work focuses on a particular application of preference ranking, wherein the problem is to learn a mapping from instances to rankings over a finite set of labels, i.e. label ranking. Our approach is based on a learning reduction technique and provides such a mapping in the form of logical rules: if [antecedent] then [consequent], where [antecedent] contains a set of conditions, usually connected by a logical conjunction operator (AND) while [consequent] consists in a ranking among labels. The approach presented in this paper mainly comprises five phases: preprocessing, rules generation, post-processing, classification and ranking generation.
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Gurrieri, M., Siebert, X., Fortemps, P., Greco, S., Słowiński, R. (2012). Label Ranking: A New Rule-Based Label Ranking Method. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances on Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31709-5_62
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DOI: https://doi.org/10.1007/978-3-642-31709-5_62
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