On the Weighted Dynamic Classifier Selection with Local Accuracies

A.I. Morales, R.M. Valdovinos (Mexico), and J.S. Sánchez (Spain)



When a multiple classifier system is employed, one of the most popular methods to perform the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting generally results affected negatively. In the present paper, we propose a weighting function based on the distance of the nearest neighbors for the Dynamic Clas sifier Selection with Local Accuracy (DCS-LA) algorithm. Experimental results with several real-problem data sets taken from the UCI Machine Learning Database Reposi tory demonstrate the advantageous of this strategy over the simple voting and the plain (unweighted) DCS-LA.

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