Combined Approach for Improving Accuracy of Prototype Selection for k-NN Classifier
Keywords:
k-nearest neighbor classifier, data reduction, prototype selection, efficiencyAbstract
The k-nearest-neighbour classifier is a powerful tool for multiclass classification and thus widely used in data mining techniques. But it consists of some severe drawbacks: high storage requirements, low noise tolerance and low efficiency in classification response. The solution to these drawbacks is to apply nearest neighbor on the reduced dataset which can be obtained by applying Prototype Selection methods on original training dataset. Various Prototype Selection methods have been developed yet but are not that efficient to overcome all the drawbacks simultaneously. So here is an attempt to build relatively more efficient algorithm by combining two or three previously developed approaches.
References
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