Feature Specific Optimal Random Forest Algorithm for Enhancing Classification Accuracy
T. Ravichandran1, Krishna Mohanta2, C. Nalini3

1T. Ravichandran, Research scholar, Bharath Institute of Higher Education and Research. Chennai, Tamil Nadu, India.

2Dr. Krishna Mohanta, Associate Professor, Department of CSE, Kakatiya Institute of Technology and Science. Chennai, Tamil Nadu, India.

3Dr. C. Nalini, Professor, Department of CSE, Bharath Institute of  Higher Education and Research. Chennai, Tamil Nadu, India.

Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 31 August 2019 | PP: 467-470 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I10990789S219/19©BEIESP DOI: 10.35940/ijitee.I1099.0789S219

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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: Creature an ensemble method, Random Forest create numerous DTs as base classifiers and invoke larger part voting to consolidate the results of the base trees. In this exploration work an endeavor is made to improve execution of Random Forest classifiers as far as correctness and time required for erudition and classification. we first present another variety of Optimal irregular Forest reliant on a direct classifier, by then build up a group classifier subject to the blend of a brisk neural Network (NN), vector-utilitarian association arrange and Optimal arbitrary Forests. Arbitrary Vector have a rich close structure game plan with incredibly short preparing time. The observational assessment and consequences of tests finished in this investigation work lead to reasonable learning and arrangement using RF

Keywords: Feature Specific, Enhancing Classification, investigation work, Arrangement
Scope of the Article: Classification