A Local Mean Representation-based K-Nearest Neighbor Classifier

K-nearest neighbor classification method (KNN), as one of the top 10 algorithms in data mining, is a very simple and yet effective nonparametric technique for pattern recognition. However, due to the selective sensitiveness of the neighborhood size k, the simple majority vote, and the conventional metric measure, the KNN-based classification performance can be easily degraded, especially in the small training sample size cases. In this article, to further improve the classification performance and overcome the main issues in the KNN-based classification, we propose a local mean representation-based k-nearest neighbor classifier (LMRKNN). In the LMRKNN, the categorical k-nearest neighbors of a query sample are first chosen to calculate the corresponding categorical k-local mean vectors, and then the query sample is represented by the linear combination of the categorical k-local mean vectors; finally, the class-specific representation-based distances between the query sample and the categorical k-local mean vectors are adopted to determine the class of the query sample. Extensive experiments on many UCI and KEEL datasets and three popular face databases are carried out by comparing LMRKNN to the state-of-art KNN-based methods. The experimental results demonstrate that the proposed LMRKNN outperforms the related competitive KNN-based methods with more robustness and effectiveness.