A novel feature ranking criterion for supervised interval valued feature selection for classification

Vinay Kumar, N. and Guru, D. S. (2017) A novel feature ranking criterion for supervised interval valued feature selection for classification. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 9-15 Nov. 2017, Kyoto, Japan.

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Official URL: https://dx.doi.org/ 10.1109/ICDAR.2017.331

Abstract

In this paper, a novel feature ranking criterion for selecting interval valued features in supervised environment is introduced. The introduced feature ranking criterion works on uni-variate interval valued data. Each feature is evaluated and associated with a score using the proposed ranking criterion. Subsequently the features are sorted based on their scores. A feature sub-setting is accomplished by considering the top d' features where d' is empirically selected. The introduced feature selection criterion is validated using a suitable symbolic classifier on relatively large dataset of flowers and water dataset. The experimental results show the superiority of the proposed feature ranking criterion stating that it outperforms the state-of-the-art feature selection methods both in-terms of dimension and classification accuracy.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: feature selection;learning (artificial intelligence);pattern classification;supervised interval valued feature selection;interval valued features;uni-variate interval valued data;feature selection criterion;feature selection methods;feature ranking criterion;Feature extraction;Computational modeling;Data models;Filtering algorithms;Testing;Covariance matrices;Numerical models;Feature ranking criterion;Interval valued feature selection;Symbolic classifier
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: MUL SWAPNA user
Date Deposited: 06 Jul 2019 05:45
Last Modified: 06 Jul 2019 05:45
URI: http://eprints.uni-mysore.ac.in/id/eprint/4803

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