Nearest Neighbor Classification with Improved Weighted Dissimilarity Measure
نویسندگان
چکیده
The usefulness and the efficiency of the k nearest neighbor classification procedure are well known. A less sophisticated method consists in using only the first nearby prototype. This means k=1 and it is the method applied in this paper. One way to get a proper result is to use weighted dissimilarities implemented with a distance function of the prototype space. To improve the classification accuracy and to minimize the number of parameters, functions that shape the relation of the dissimilarity with the prototypes, the features or the class belonging are proposed. Benchmark tests are also presented and the worth results encourage us to continue developing this new proposed weighted model.
منابع مشابه
A class-dependent weighted dissimilarity measure for nearest neighbor classification problems
A class-dependent weighted (CDW) dissimilarity measure in vector spaces is proposed to improve the performance of the nearest neighbor classifier. In order to optimize the required weights, an approach based on Fractional Programming is presented. Experiments with several standard benchmark data sets show the effectiveness of the proposed technique.
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