FSEFST:Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data
Radhika K R1, Pushpa C N2, Thriveni J3, Venugopal K R4
1Radhika K R*, Department of CSE, Bangalore University, Bangalore, India,.
2Pushpa C N, Department of CSE, UVCE, Bangalore University, Bangalore, India.
3Dr.Thriveni J, Department of CSE, UVCE, Bangalore University, Bangalore, India.
4Dr. Venugopal K R, Vice-Chancellor, Bangalore University, Bangalore, India.
Manuscript received on November 14, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 814-820 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6907129219/2019©BEIESP | DOI: 10.35940/ijitee.B6907.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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
Keywords: Dimensionality Reduction, Feature Extraction, Feature Selection, Feature Subset, High Dimensional Data.
Scope of the Article: Knowledge Engineering Tools and Techniques