A Framework for Categorize Feature Selection Algorithms for Classification and Clustering

Abstract

At first, one of the dimension reduction techniques so called feature selection is explained. The Concepts, principles and existing feature selection methods for classification and clustering are also described. Then, a categorizing framework consisting of the procedures of finding selected subsets, including Search-based procedures and non-search based, evaluation criteria and data mining tasks will be completed and developed. During the grouping of Feature selection algorithms, categorizing framework represent guidelines to select appropriate algorithm(s) for each application. In categorizing, similar algorithms which follow the same process of selected subset finding and have the same evaluation criteria, are placed in the one block. Empty blocks indicates that no algorithm has been designed for them and this is a motive to design new algorithm.