Title

Confusion Matrix-Based Feature Selection

Document Type

Conference Proceeding

Publication Date

2011

Abstract

This paper introduces a new technique for feature selection and illustrates it on a real data set. Namely, the proposed approach creates subsets of attributes based on two criteria: (1) individual attributes have high discrimination (classification) power; and (2) the attributes in the subset are complementary - that is, they misclassify different classes. The method uses information from a confusion matrix and evaluates one attribute at a time. Keywords: classification, attribute selection, confusion matrix, k-nearest neighbors;.

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