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Application of Nyaya Inference Method for Feature Selection and Ranking in Classification Algorithms

Publication Type : Conference Paper

Thematic Areas : Amrita e-Learning Research Lab

Publisher : 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017

Source : 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Institute of Electrical and Electronics Engineers Inc. (2017)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042739227&doi=10.1109%2fICACCI.2017.8125986&partnerID=40&md5=78c8fe50b7fddb7c22c77f647d2ba897

ISBN : 9781509063673

Keywords : Artificial intelligence, Attribute selection, Classification (of information), Classification algorithm, Classification models, Computation theory, Feature extraction, Feature selection algorithm, Inference engines, Inference methods, Information Gain, Learning algorithms, Learning systems, Principal component analysis, Quality of data, Supervised learning, Two-class classification problems

Campus : Amritapuri

School : School of Engineering

Center : E-Learning

Department : E-Learning, Electrical and Electronics

Year : 2017

Abstract : The success of machine learning (ML) algorithms depends on the quality of data given to them. If the input data contains insufficient or irrelevant features, the accuracy of machine learning algorithm decreases. Attribute selection has a key role in creation of classification models. Based on the 'logic behind the inference' principle in the Nyaya school of thought, this paper proposes a new method - Nyaya Inference Method for Attribute selection and Ranking (NIMAR), for feature selection for the two class classification problem. The Anvayavyatireka Vyapti and hetvabhasa methodologies of the Nyaya school of thought were used for ranking attributes, and removal of irrelevant attributes, respectively. The NIMAR approach showed a remarkable improvement in accuracy compared to the principal component analysis (PCA) method and as good as the Relief feature selection algorithm and the information gain (IG) methods

Cite this Research Publication : K. Seena and Sundaravardhan, R., “Application of Nyaya Inference Method for Feature Selection and Ranking in Classification Algorithms”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017.

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