Abstract
In this work, a new method for class-specific feature selection, which selects a possible different feature subset for each class of a supervised classification problem, is proposed. Since conventional classifiers do not allow using a different feature subset for each class, the use of a classifier ensemble and a new decision rule for classifying new instances are also proposed. Experimental results over different databases show that, using the proposed method, better accuracies than using traditional feature selection methods, are achieved.
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Pineda-Bautista, B.B., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2009). Taking Advantage of Class-Specific Feature Selection. 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_1
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DOI: https://doi.org/10.1007/978-3-642-04394-9_1
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