Effective Feature Selection Strategy Using Manova Test
V. Sathya Durga1, Thangakumar Jeyaprakash2
1V. Sathya Durga, Research Scholar, Department of Computer Science Engineering, Hindustan Institute of Technology and Science, Padur, India.
2Thangakumar Jeyaprakash, Associate Professor, Department of Computer Science Engineering, Hindustan Institute of Technology and Science, Padur, India.
Manuscript received on 18 March 2019 | Revised Manuscript received on 22 March 2019 | Manuscript published on 30 July 2019 | PP: 5969-5971 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3654078219/2019©BEIESP | DOI: 10.35940/ijrte.B3654.078219
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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: Feature selection is the most important step to develop any latest learning model. As the complexity of the leaning models increases day by day there is an increasing demand, in selecting the right features to build the model. There are many methods for feature selection. A new feature selection based on the Manova statistical test is implemented. Using the Manova test, we select attributes from academic datasets. Using the selected attributes, we build a classification model. Accuracy of the model with feature selection is compared with a model with all attributes. Results are discussed. It is proved that the classification model build with features selected by Manova test achieves more accuracy than a model built with all features.
Index Terms: Feature Selection, Manova, Wiki Lambda.
Scope of the Article: Classification