Feature Selection Method based on Fisher’s Exact Test for Agricultural Data
S. Rajeswari1, K. Suthendran2

1S. Rajeswari, Department of Computer Applications, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2K. Suthendran, Department of Information Technology, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
Manuscript received on 01 December 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 31 December 2019 | PP: 558-564 | Volume-8 Issue-4S2 December 2019 | Retrieval Number: D11041284S219/2019©BEIESP | DOI: 10.35940/ijrte.D1104.1284S219
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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: This paper is aimed to analyze the feature selection process based on different statistical methods viz., Correlation, Gain Ratio, Information gain, OneR, Chi-square MapReduce model, Fisher’s exact test for agricultural data. During the recent past, Fishers exact test was commonly used for feature selection process. However, it supports only for small data set. To handle large data set, the Chi square, one of the most popular statistical methods is used. But, it also finds irrelevant data and thus resultant accuracy is not as expected. As a novelty, Fisher’s exact test is combined with Map Reduce model to handle large data set. In addition, the simulation outcome proves that proposed fisher’s exact test finds the significant attributes with more accurate and reduced time complexity when compared to other existing methods.
Keywords: Fisher’s Exact Test, Feature Selection, Map Reduce, Chi-square Test, Statistical Test.
Scope of the Article: Data Visualization