Machine Learning Based Supervised Feature Selection Algorithm for Data Mining
K. Sutha1, J. Jebamalar Tamilselvi2
1K. Sutha Research Scholar, Bharathiar University, Coimbatore, (Tamil Nadu), India.
2Dr. J. Jebamalar Tamilselvi, Professor, Department of MCA, Jaya Engineering College, Chennai, (Tamil Nadu), India.
Manuscript received on 21 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript published on 30 August 2019 | PP: 3396-3401 | Volume-8 Issue-10, August 2019 | Retrieval Number: J94830881019/19©BEIESP | DOI: 10.35940/ijitee.J9483.0881019
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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: IData Scientists focus on high dimensional data to predict and reveal some interesting patterns as well as most useful information to the modern world. Feature Selection is a preprocessing technique which improves the accuracy and efficiency of mining algorithms. There exist a numerous feature selection algorithms. Most of the algorithms failed to give better mining results as the scale increases. In this paper, feature selection for supervised algorithms in data mining are considered and given an overview of existing machine learning algorithm for supervised feature selection. This paper introduces an enhanced supervised feature selection algorithm which selects the best feature subset by eliminating irrelevant features using distance correlation and redundant features using symmetric uncertainty. The experimental results show that the proposed algorithm provides better classification accuracy and selects minimum number of features.
Index Terms: Feature Selection, Supervised Learning, Data Mining
Scope of the Article: Data Mining