Open AccessFSEFST:Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data
Author(s) -
R. Radhika,
C N Pushpa,
Dr.Thriveni J,
Vice-Chancellor Dr. Venugopal K R
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b6907.129219
Subject(s) - dimensionality reduction , feature selection , feature extraction , pattern recognition (psychology) , computer science , curse of dimensionality , feature (linguistics) , artificial intelligence , minimum redundancy feature selection , data mining , selection (genetic algorithm) , philosophy , linguistics
Dimensionality reduction is one of the pre-processing phases required when large amount of data is available. Feature selection and Feature Extraction are one of the methods used to reduce the dimensionality. Till now these methods were using separately so the resultant feature contains original or transformed data. An efficient algorithm for Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data (FSEFST) has been proposed in order to select and extract the efficient features by using feature subset method where it will have both original and transformed data. The results prove that the suggested method is better as compared with the existing algorithm