Distance Measure Assisted Rough Set Feature Selection

Feature Selection (FS) is a technique for dimensionality reduction. Its aims are to select a subset of the original features of a dataset which are rich in the most useful information. The benefits include improved data visualisation, transparency, a reduction in training and utilisation times and potentially, improved prediction performance. Many approaches based on rough set theory have employed the dependency function which is based on the information contained in the lower approximation as an evaluation step in the FS process with much success. This paper presents a novel rough set FS technique which uses the information of both the lower approximation dependency value and a distance metric for the consideration of objects in the boundary region. The use of this measure in rough set feature selection can result in smaller subset sizes than those obtained using the dependency function alone.