Feature subset selection using granular information

Studies in machine learning, data mining, and pattern classification often use a technique to select relevant features from a large data set. This technique is known as Feature subset selection. This feature selection technique is performed in order to reduce hypothesis search space, to reduce storage, and enhance the performance of the data mining, or machine learning algorithms. In recent years researchers have been actively involved and are focusing on this particular problem from the perspective of machine learning. This paper will briefly study the existing approaches to select features. Also, we will study the effectiveness of granular information to feature selection. We will also propose a simple feature elimination based algorithm that uses granular information.