Feature Selection Using Mfcm (Modified Fuzzy C-Mean) & Classification Using Apso (Accelerated Particle Swarm Optimization)

Abstract

Feature selection, also known as dimensionality reduction, is a common preprocessing step in the fields of pattern recognition, data mining, and machine learning. This is a critical issue when mining high-dimensional, massive data sets. Preprocessing the data before analysis to acquire a smaller collection of representative features and keeping the optimal salient properties of the data leads to more compactness of the models trained and better generalization, as well as a decrease in processing time. Therefore, the standard for dimension reduction is to save just the information that is most important from the original data, as determined by certain optimality criteria. In this paper we are going to form a new framework include the combination of MFCM and APSO, to find the HIDS.