Application of Feature Weighting for the Intensification of Data Classification
J. Arunadevi1, K. Ganeshamoorthi2, R. Rampriya3
1Dr. J. Arunadevi, Assistant Professor, Department of Computer Science, Raja Doraisingam Govt. Arts College, Sivaganga, Affilliated to Alagappa Univesity, (Tamil Nadu), India.
2K. Ganeshamoorthi, Ph.D Scholar Full-Time, Department of Computer Science, Raja Doraisingam Govt. Arts College, Sivaganga, Affilliated to Alagappa Univesity, (Tamil Nadu), India.
3R. Rampriya, M.Phil Scholar Full-Time, Department of Computer Science, Raja Doraisingam Govt. Arts College, Sivaganga, Affilliated to Alagappa Univesity, (Tamil Nadu), India.
Manuscript received on 09 December 2019 | Revised Manuscript received on 21 December 2019 | Manuscript Published on 30 December 2019 | PP: 879-887 | Volume-9 Issue-2S2 December 2019 | Retrieval Number: B11381292S219/2019©BEIESP | DOI: 10.35940/ijitee.B1138.1292S219
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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: Classification is the supervised learning technique which is applied in many of the real time applications. In this study we have considered three classifiers which are widely used and then the intensification of the classifiers are considered. Among various methods to improve the performance of the classifiers, this research concentrate on the feature weighting techniques applied for the classifiers. This analysis is done based on the results obtained from the Rapidminer tool. Here we have deployed four feature weighting techniques for the intensification of the three classifiers. It is tested with three dataset. The experimental environment and the results are discussed in detail.
Keywords: Feature Weighting, Decision Tree, KNN, Naïve Bayes.
Scope of the Article: Classification