Abstract Neighborhood classifiers

K nearest neighbor classifier (K-NN) is widely discussed and applied in pattern recognition and machine learning, however, as a similar lazy classifier using local information for recognizing a new test, neighborhood classifier, few literatures are reported on. In this paper, we introduce neighborhood rough set model as a uniform framework to understand and implement neighborhood classifiers. This algorithm integrates attribute reduction technique with classification learning. We study the influence of the three norms on attribute reduction and classification, and compare neighborhood classifier with KNN, CART and SVM. The experimental results show that neighborhood-based feature selection algorithm is able to delete most of the redundant and irrelevant features. The classification accuracies based on neighborhood classifier is superior to K-NN, CART in original feature spaces and reduced feature subspaces, and a little weaker than SVM