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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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: | C Swapna Library Assistant |
| 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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