International Journal of Computer Science and Network Security
Feature selection is an important topic in data mining, especially for high dimensional datasets. Feature selection (also known as subset selection) the best subset contains the least number of dimensions that most contribute to accuracy; they discard the remaining, unimportant dimensions. This is an important stage of preprocessing and is one of two ways of avoiding the curse of dimensionality (the other is feature extraction). There are two approaches in feature selection known as forward selection and backward selection. Feature selection has been an active research area in pattern recognition, statistics and data mining communities.