关键词: KNN算法, 互近邻, 类可信度, 模式分类

Abstract:

Since the Pseudo-Nearest Neighbor classification algorithm（LMPNN） is sensitive to the outliers and noise points in data, this paper tries to make the improvement on LMPNN and proposes a Bidirectional Selection-based Pseudo Nearest Neighbor algorithm（BS-PNN）. Firstly, the [k] Nearest Neighbors are selected by using the proximity measure; and then, the test sample and the neighbor samples are selected bidirectionally through the mutual neighbor definition. Secondly, by calculating both the number of neighbors and the weighted distance of the local mean of the neighbors in each class, the Euclidean distance between the test sample and the pseudo-neighbor is obtained. Finally, the test sample is classified by the voting method which uses an improved class credibility measure. The proposed method has the advantages of being able to accurate identification of noise points, reduce the sensitivity of the number of nearest neighbor [k], and improve the classification accuracy when dealing with complex classification tasks. The simulation experiments have been performed on 15 real data sets of UCI and KEEL, and KNN, WKNN, LMKNN, PNN, LMPNN, DNN and P-KNN algorithms have been employed to compare with the proposed algorithm. The experimental results show that the classification performance of the proposed bidirectional selection-based pseudo nearest neighbor algorithm is significantly better than several other neighbor classification algorithms.

Key words: K Nearest Neighbors（KNN）, mutual neighbor, class credibility, pattern classification