Feature Selection with High-Dimensional Imbalanced Data

Abstract

Feature selection is an important topic in data mining, especially for high dimensional datasets. Filtering techniques in particular have received much attention, but detailed comparisons of their performance is lacking. This work considers three filters using classifier performance metrics and six commonly-used filters. All nine filtering techniques are compared and contrasted using five different microarray expression datasets. In addition, given that these datasets exhibit an imbalance between the number of positive and negative examples, the utilization of sampling techniques in the context of feature selection is examined.