Modified fuzzy C-means algorithm for feature selection
In this paper we propose a novel method for feature selection based on a modified fuzzy C-means algorithm with supervision (MFCMS). MFCMS adopts an appropriately modified version of the objective function used by the classic fuzzy C-means. We applied MFCMS to some real-world pattern classification b...
Gespeichert in:
| Veröffentlicht in: | Proceedings - IEEE ATM Workshop |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
01.01.2000
|
| ISSN: | 1098-7789 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Zusammenfassung: | In this paper we propose a novel method for feature selection based on a modified fuzzy C-means algorithm with supervision (MFCMS). MFCMS adopts an appropriately modified version of the objective function used by the classic fuzzy C-means. We applied MFCMS to some real-world pattern classification benchmarks. To test the effectiveness of MFCMS as feature selector, we used the well-known k-nearest neighbor as learning algorithm. In our experiments we found that the classification performance using the set of features selected by MFCMS is better than that using all the original features. Furthermore, our approach proved to be less time-consuming than other feature selection methods. |
|---|---|
| Bibliographie: | SourceType-Scholarly Journals-2 ObjectType-Feature-2 ObjectType-Conference Paper-1 content type line 23 SourceType-Conference Papers & Proceedings-1 ObjectType-Article-3 |
| ISSN: | 1098-7789 |