Confusion Matrix-Based Feature Selection
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;.
Visa, Sofia; Ramsay, B.; Ralescu, A.; and VanDerKnaap, E., "Confusion Matrix-Based Feature Selection" (2011). Proceedings of The 22nd Midwest Artificial Intelligence and Cognitive Science Conference 2011, , 120-127. Retrieved from https://openworks.wooster.edu/facpub/88