Estimating the posterior probabilities using the k-nearest neighbor rule |
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Authors: | Atiya Amir F |
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Affiliation: | Department of Computer Engineering, Cairo University, Giza, Egypt. amir@alumni.caltech.edu |
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Abstract: | In many pattern classification problems, an estimate of the posterior probabilities (rather than only a classification) is required. This is usually the case when some confidence measure in the classification is needed. In this article, we propose a new posterior probability estimator. The proposed estimator considers the K-nearest neighbors. It attaches a weight to each neighbor that contributes in an additive fashion to the posterior probability estimate. The weights corresponding to the K-nearest-neighbors (which add to 1) are estimated from the data using a maximum likelihood approach. Simulation studies confirm the effectiveness of the proposed estimator. |
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