| Autor: |
Senthamilarasu, S., Hemalatha, M. |
| Zdroj: |
2013 IEEE International Conference on Computational Intelligence & Computing Research; 2013, p1-4, 4p |
| Abstrakt: |
The emergence in the data mining world single classifier is not sufficient for classifying the data. Because of the availability of large datasets does not execute within the time and get the classification accuracy is low compare than ensemble classifier. In this paper, we make extensive study of different methods for building ensemble classifier. In this proposed work, a novel approach which uses an Intuitionistic fuzzy version of k-means has been introduced for grouping interdependent features. The proposed method achieves improvement in classification accuracy and perhaps to select the least number of features which show the way to simplification of learning task to a big extent. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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