An affinity-based new local distance function and similarity measure for kNN algorithm |
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Authors: | Gautam Bhattacharya |
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Affiliation: | a Department of Physics, University Institute of Technology, University of Burdwan, Golapbag (North), Burdwan 713104, India b Department of Mathematics, University Institute of Technology, University of Burdwan, Golapbag (North), Burdwan 713104, India c Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India |
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Abstract: | In this paper, we propose a modified version of the k-nearest neighbor (kNN) algorithm. We first introduce a new affinity function for distance measure between a test point and a training point which is an approach based on local learning. A new similarity function using this affinity function is proposed next for the classification of the test patterns. The widely used convention of k, i.e., k = [√N] is employed, where N is the number of data used for training purpose. The proposed modified kNN algorithm is applied on fifteen numerical datasets from the UCI machine learning data repository. Both 5-fold and 10-fold cross-validations are used. The average classification accuracy, obtained from our method is found to exceed some well-known clustering algorithms. |
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Keywords: | kNN Affinity function Similarity measure |
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