Feature Selection Method to Improve the Accuracy of Classification Algorithm
Rajit Nair1, Amit Bhagat2
1Rajit Nair, Ph.D, Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal Public Technical University, Bhopal (Madhya Pradesh), India.
2Dr. Amit Bhagat, Assistant Professor, Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal Public Technical University, Bhopal (Madhya Pradesh), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 124-127 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3421048619/19©BEIESP
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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: Today, we are living in the era of big data and it is not an easy task to process big data. Big data are also known as high dimensional data, so to reduce this high dimensional data there are two methods one is feature selection and the other one is feature extraction. This paper present the methods based on feature selection which are selectkbest and selectpercentile. This work also shows how feature selection works and how it helps during classification process. There are mainly three feature selection methods one is univariate, other one is model based feature selection and the last one is iterative method. In this paper it has been shown that how accuracy has been improved in classification algorithms used in machine learning through these feature selection methods. The proposed work has increased the classification accuracy for the algorithm like Naïve Bayes, Support Vector Machine, Logistic regression and K- Nearest Neighbor. Comparing to all other algorithms Logistic Regression has achieved higher accuracy with 96.9%.
Keyword: Dimensions, Preprocessing, Datasets, Classification, Big Data.
Scope of the Article: Classification