Application of k - Nearest Neighbor on FeatureProjections Classi er to Text

نویسنده

  • Tuba Yavuz
چکیده

This paper presents the results of the application of an instance-based learning algorithm k-Nearest Neighbor Method on Feature Projections (k-NNFP) to text categorization and compares it with k-Nearest Neighbor Classiier (k-NN). k-NNFP is similar to k-NN except it nds the nearest neighbors according to each feature separately. Then it combines these predictions using a majority voting. This property causes k-NNFP to eliminate possible adverse eeects of irrelevant features on the classiication accuracy. Experimental evidence indicates that k-NNFP is superior to k-NN in terms of classiication accuracy in the presence of irrelevant features in many real world domains.

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تاریخ انتشار 1998