Open AccessSelection of Features on Mining Techniques for Classification
Author(s) -
G. Sai Chaitanya Kumar,
Sibin Mohan,
G. Prabakaran
Publication year - 2018
Publication title -
asian journal of computer science and technology
Language(s) - English
Resource type - Journals
eISSN - 2583-7907
pISSN - 2249-0701
DOI - 10.51983/ajcst-2018.7.s1.1793
Subject(s) - feature selection , computer science , euclidean distance , data mining , selection (genetic algorithm) , class (philosophy) , artificial intelligence , feature (linguistics) , clarity , pattern recognition (psychology) , one class classification , set (abstract data type) , statistical classification , machine learning , support vector machine , philosophy , biochemistry , linguistics , chemistry , programming language
Feature selection has been developed by several mining techniques for classification. Some existing approaches couldn’t remove the irrelevant data from dataset for class. Thus it needs the selection of appropriate features that emphasize its role in classification. For this it consider the statistical method like correlation coefficient to identify the features from feature set whose data are very important for existing classes. The several methods such as Gaussian process, linear regression and Euclidean distance have taken into consideration for clarity of classification. The experimental results reveal that the proposed method identifies the exact relevant features for several classes.