Review On Feature Selection Techniques in Data Mining - Ramadass, S; Gunasekaran, M (International Journal of Computer Sciences and Engineering, 2025)
Feature selection is a data pre-processing technique specially used for classification problems. It aims at identifying the minimal reduct with less number of features without affecting the classification accuracy of the data set. Its goal is to choose a negligible subset of features as indicated by some sensible criteria with the goal that the first undertaking can be accomplished similarly well, if worse. By picking an insignificant subset of features, unimportant and repetitive features are evacuated by the paradigm. Rough set theory is a technique that has been used for feature selection. It is utilizing to find the basic relationship from the uproarious data, which is utilizing the discritization strategy on discrete-esteemed properties and proceeds with values quality. It depends on making the equalance classes with in the given data, every one of the data tupels are making an equalance classes are indiscernalbe with the regard of the properties depicting data. Though there is many rough set based approaches like quick reduct, relative reduct entropy based reduct, these approaches are able to identify a reduct set. This paper presents a survey on various methods and techniques of feature selection and its advantages and disadvantages.