Abstract
This paper proposes a classifier that uses fuzzy rough set theory to improve the Fuzzy Nearest Neighbour (FNN) classifier. We show that previous attempts to use fuzzy rough set theory to improve the FNN algorithm have some shortcomings and we overcome them by using the fuzzy positive region to measure the quality of the nearest neighbours in the FNN classifier. A preliminary experimental evaluation shows that the new approach generally improves upon existing methods.
| Original language | English |
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| Title of host publication | 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
| Place of Publication | NEW YORK |
| Publisher | IEEE Press |
| Pages | 1961-1967 |
| Number of pages | 7 |
| ISBN (Print) | 978-1-4673-1506-7 |
| Publication status | Published - 2012 |
| Event | IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)/International Joint Conference on Neural Networks (IJCNN)/IEEE Congress on Evolutionary Computation (IEEE-CEC)/IEEE World Congress on Computational Intelligence (IEEE-WCCI) - Brisbane Duration: 10 Jun 2012 → 15 Jun 2012 |
Conference
| Conference | IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)/International Joint Conference on Neural Networks (IJCNN)/IEEE Congress on Evolutionary Computation (IEEE-CEC)/IEEE World Congress on Computational Intelligence (IEEE-WCCI) |
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| City | Brisbane |
| Period | 10 Jun 2012 → 15 Jun 2012 |