Feature Selection Algorithms for Wireless Sensor Networks

Abstract

The task of selecting relevant features in classification problems can be viewed as one of the most fundamental problems in the field of 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; we 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.

The main idea of feature selection is to choose a subset of input variables by eliminating features with little or no predictive information. Feature selection is a preprocessing phase in an intrusion detection system in wireless sensor network. As when we make clustering to sensor nodes to discover anomaly we must doing this preprocessing phase to avoid curse of dimensionality problem and this preprocessing phase will reduce complexity of clustering algorithm.