Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Visualization and Aggregation of Nearest Neighbor Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving nearest neighbor rule with a simple adaptive distance measure
Pattern Recognition Letters
Multiscale Classification Using Nearest Neighbor Density Estimates
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
The Nearest Neighbor (NN) rule is one of the simplest and most effective pattern classification algorithms. In basic NN rule, all the instances in the training set are considered the same to find the NN of an input test pattern. In the proposed approach in this article, a local weight is assigned to each training instance. The weights are then used while calculating the adaptive distance metric to find the NN of a query pattern. To determine the weight of each training pattern, we propose a learning algorithm that attempts to minimize the number of misclassified patterns on the training data. To evaluate the performance of the proposed method, a number of UCI-ML data sets were used. The results show that the proposed method improves the generalization accuracy of the basic NN classifier. It is also shown that the proposed algorithm can be considered as an effective instance reduction technique for the NN classifier.