Experiments with cost-sensitive feature evaluation

Abstract

Many machine learning tasks contain feature evaluation as one of its important components. This work is concerned with attribute estimation in the problems where class distribution is unbalanced or the misclassification costs are unequal. We test some common attribute evaluation heuristics and propose their cost-sensitive adaptations. The new measures are tested on problems which can reveal their strengths and weaknesses.