Open AccessFeature Selection Method based on Fisher’s Exact Test for Agricultural Data
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d1104.1284s219
Subject(s) - exact test , computer science , feature selection , fisher information , fisher kernel , feature (linguistics) , data set , data mining , statistical hypothesis testing , chi square test , set (abstract data type) , test data , algorithm , pearson's chi squared test , test set , statistics , artificial intelligence , pattern recognition (psychology) , mathematics , machine learning , test statistic , linguistics , philosophy , kernel fisher discriminant analysis , facial recognition system , programming language
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.