Maximizing Diversity Among k-NN Classifiers, An Experimental Study
نویسنده
چکیده
Bagging and random feature subsets are used simultaneously to achieve maximum diversity among k-NN component experts in a fusion system. Experimental results indicate that for certain data the combination of the two design methods in a fusion system is beneficial. We also compare two random feature subset design methods, a widely used, conventional, expert based method and a system based design method proposed by us. The system based method shows superior performance when more than 20 percent of the features have discriminatory information..
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