Abstract
In this paper we present a Gene Expression Programming algorithm for multi-label classification. This algorithm encodes each individual into a discriminant function that shows whether a pattern belongs to a given class or not. The algorithm also applies a niching technique to guarantee that the population includes functions for each existing class. Our proposal has been compared with some recently published algorithms. The results on several datasets demonstrate the feasibility of this approach to tackle with multi-label problems.
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Ávila, J.L., Gibaja, E.L., Zafra, A., Ventura, S. (2009). A Niching Algorithm to Learn Discriminant Functions with Multi-Label Patterns. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_69
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DOI: https://doi.org/10.1007/978-3-642-04394-9_69
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