Featuring the attributes in supervised machine learning

Abstract

This paper introduces an approach to feature subset selection which is able to characterise the attributes of a supervised machine learning problem into two categories: essential and important features. Additionally, the fusion of both kinds of features yields to an overcoming in the prediction task, where some measures such as accuracy and Receiver Operating Characteristic curve (ROC) have been reported. The test-bed is composed of eight binary and multi-class classification problems with up to five hundred of attributes. Several classification algorithms such as Ridor, PART, C4.5 and NBTree have been tested to assess the proposal.