An Improved Neural Network Classifier Using Fuzzy Nearest Neighbor Partitioning Method

In this study, a novel method, fuzzy nearest neighbor partitioning (FNNP), is proposed for improving the classification capability of the neural network. For the original nearest neighbor partitioning method, the “short-sightedness defect” problem restrains the nearest neighbor partitioning method to learn valuable experience from the distribution information outside the samples’ nearest neighbors. Moreover, the noise sensitive problem that exists in the process of classifying the unknown samples further influences the performance of the nearest neighbor partitioning method. To overcome the short-sightedness defect problem, fuzzy logic theory is integrated into the FNNP, and promotes the ability that the FNNP learns experience from extensive distribution information. In addition, an improved classification strategy, which adopts the concept of the fuzzy nearest neighbor, is employed to increase the immunity of FNNP to noise when the unknown samples are classified. The proposed FNNP is compared with other classification methods for some famous datasets. The results of the experiments indicate that the proposed method achieves promising classification performance for various indicators.