Survey on Feature Selection for Data Reduction

Abstract

The storage capabilities and advanced in data collection has led to an information load and the size of databases increases in dimensions, not only in rows but also in columns. Data reduction (DR) plays a vital role as a data prepossessing techniques in the area of knowledge discovery from the huge collection of data. Feature selection (FS) is one of the well known data reduction techniques, which deals with the reduction of attributes from the original data without affecting the main information content. Based on the training data used for different applications of knowledge discovery, FS technique falls into supervised, unsupervised. In this paper an extensive survey on supervised FS technique describing the different searching approach, methods and application areas with an outline of a comparative study is covered.