Guru, D. S. and Kumar, N. V. and Suhil, M. (2017) Feature selection of interval valued data through interval k-means clustering. International Journal Of Computer Vision And Image Processing, 7 (2). pp. 64-80. ISSN 2155-6989
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This paper introduces a novel feature selection model for supervised interval valued data based on interval K-Means clustering. The proposed model explores two kinds of feature selection through feature clustering viz., class independent feature selection and class dependent feature selection. The former one clusters the features spread across all the samples belonging to all the classes, whereas the latter one clusters the features spread across only the samples belonging to the respective classes. Both feature selection models are demonstrated to explore the generosity of clustering in selecting the interval valued features. For clustering, the kernel of the K-means clustering has been altered to operate on interval valued data. For experimentation purpose four standard benchmarking datasets and three symbolic classifiers have been used. To corroborate the effectiveness of the proposed model, a comparative analysis against the state-of-the-art models is given and results show the superiority of the proposed model.
| Item Type: | Article |
|---|---|
| Divisions: | Department of > Computer Science |
| Depositing User: | C Swapna Library Assistant |
| Date Deposited: | 13 Jun 2019 05:19 |
| Last Modified: | 13 Jun 2019 05:19 |
| URI: | http://eprints.uni-mysore.ac.in/id/eprint/2999 |
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