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.
A modified fuzzy C-means algorithm for feature selection
FROSINI, GRAZIANO;LAZZERINI, BEATRICE;MARCELLONI, FRANCESCO
2000-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
