Improved K- Nearest Neighbor Classification

K- nearest neighbor classification is widely used in pattern recognition, data mining,machine learning and other fields for its intuitive and simple. Its application has been involved in bioinformatics certification, classification images, face recognition and other fields.This article is based on k-nearest neighbor analysis to improve k- nearest neighbor classification algorithms. There are three main works as follows:This paper is mainly based on three aspects about k- neighbor classification:1. The neighbor classification based on distance weighted intra-class neighborhood was proposed. This classification method make use of the neighboring samples of each class and the near sample have more effect on the class, thus the sample have greater weighted distance. Then this method use the neighbor classification rule to improve the classification accuracy.2. The nearest neighbor classification based on class mean was proposed. In the process of classification the accuracy of nearest neighbor classifications is easily influenced by outliers. The nearest neighbor classification based on class means make full use of the class means information and reduce the impact of outliers on the classification accuracy.3. The nearest neighbor classification based on local mean was proposed. The small number of training samples result to low classification accuracy in the process of nearest neighbor classification. In order to improve the performance of nearest neighbor classification, this classification method make full use of local mean information of test samples and improve the classification accuracy in a small sample, while preventing over-fitting.