Evaluating feature selection methods for learning in data mining applications

Recent advances in computing technology in terms of speed, cost, as well as access to tremendous amounts of computing power and the ability to process huge amounts of data in reasonable time have spurred increased interest in data mining applications. Machine learning has been one of the methods used in most of these data mining applications. The data used as input to any of these learning systems are the primary source of knowledge in terms of what is learned by these systems. There have been relatively few studies on preprocessing data used as input in these data mining systems. In this study, we evaluate several feature selection methods as to their effectiveness in preprocessing input data. We use real-world financial credit-risk data in evaluating these systems