A 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...

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Veröffentlicht in:2000 19th International Conference of the North American Fuzzy Information Processing Society S. 148 - 152
Hauptverfasser: Frosini, G., Lazzerini, B., Marcelloni, F.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 2000
Schlagworte:
Benchmark testing
Clustering algorithms
Fuzzy sets
Partitioning algorithms
Pattern classification
Pattern recognition
Prototypes
Shape
Symmetric matrices
ISBN:9780780362741, 0780362748
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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.
ISBN:9780780362741
0780362748
DOI:10.1109/NAFIPS.2000.877409