Abstract
This paper proposes an approach based on fuzzy rough set theory to improve nearest neighbor based classification. Six measures are introduced to evaluate the quality of the nearest neighbors. This quality is combined with the frequency
at which classes occur among the nearest neighbors and the similarity w.r.t. the nearest neighbor, to decide which class to pick among the neighbor’s classes. The importance of each aspect is weighted using optimized weights. An experimental study shows that our method, Quality, Frequency and Similarity based Fuzzy
Nearest Neighbor (QFSNN), outperforms state-of-the-art nearest neighbor classifiers.
at which classes occur among the nearest neighbors and the similarity w.r.t. the nearest neighbor, to decide which class to pick among the neighbor’s classes. The importance of each aspect is weighted using optimized weights. An experimental study shows that our method, Quality, Frequency and Similarity based Fuzzy
Nearest Neighbor (QFSNN), outperforms state-of-the-art nearest neighbor classifiers.
| Original language | English |
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| Title of host publication | 2013 IEEE International Conference on Fuzzy Systems (FUZZ) |
| Publisher | IEEE Press |
| Pages | 1-8 |
| ISBN (Print) | 978-1-4799-0020-6 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | Fuzzy Systems - Hyderabad, Hyderabad, India Duration: 07 Jul 2013 → 10 Jul 2013 Conference number: 22 |
Conference
| Conference | Fuzzy Systems |
|---|---|
| Abbreviated title | FUZZ-IEEE-2013 |
| Country/Territory | India |
| City | Hyderabad |
| Period | 07 Jul 2013 → 10 Jul 2013 |