Multiple Real-valued K nearest neighbor classifiers system by feature grouping

This paper proposes a method to fuse Real-valued K nearest neighbor classifier by feature grouping. Real-valued K nearest neighbor classifier can approximate continuous-valued target functions, which can provide more information than crisp K nearest neighbor classifier in fusion. In addition real-valued K nearest neighbor classifier is sensitive to feature perturbation. Therefore, when multiple real-valued K nearest neighbor classifiers are fused by feature grouping, the performance of the fusion is better than single classifier. In order to validate the performance of fusion, four datasets are selected from UCI Repository. Experimental results show that the performance of fusion is better than single classifier and multiple classifier system by other perturbations.