Abstract
Unbalanced data in a classification problem appears when there are many more instances of some classes than others. Several solutions were proposed to solve this problem at data level by under-sampling. The aim of this work is to propose evolutionary prototype selection algorithms that tackle the problem of unbalanced data by using a new fitness function. The results obtained show that a balancing of data performed by evolutionary under-sampling outperforms previously proposed under-sampling methods in classification accuracy, obtaining reduced subsets and getting a good balance on data.
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García, S., Cano, J.R., Fernández, A., Herrera, F. (2006). A Proposal of Evolutionary Prototype Selection for Class Imbalance Problems. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_168
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DOI: https://doi.org/10.1007/11875581_168
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