Recent advances in error rate estimation
Pattern Recognition Letters
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric graphs for improving nearest neighbor decision rules
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartIII
Learning pattern classification-a survey
IEEE Transactions on Information Theory
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Local estimation of posterior class probabilities to minimize classification errors
IEEE Transactions on Neural Networks
The ROC isometrics approach to construct reliable classifiers
Intelligent Data Analysis
A penalized likelihood based pattern classification algorithm
Pattern Recognition
An adaptable k-nearest neighbors algorithm for MMSE image interpolation
IEEE Transactions on Image Processing
DCPE co-training for classification
Neurocomputing
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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.