Estimating the posterior probabilities using the k-nearest neighbor rule.
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.
Atiya
Amir F
AF
Department of Computer Engineering, Cairo University, Giza, Egypt. amir@alumni.caltech.edu
D001699:Biometry; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011336:Probability