The Research on an Adaptive K-Nearest Neighbors Classifier

摘要：

K-nearest neighbor (KNNC) classifier is the most popular non-parametric classifier. But it requires much classification time to search k nearest neighbors of an unlabelled object point, which badly affects its efficiency and performance. In this paper, an adaptive k-nearest neighbors classifier (AKNNC) is proposed The algorithm can find k nearest neighbors of the unlabelled point in a small hypersphere in order to improve the efficiencies and classify the point The hyperspheres size can be automatically determined. It requires a quite moderate preprocessing effort, and the cost to classify an unlabelled point is O(ad) + O(k)(1≤ a＜＜N). Our experiment shows the algorithm performance is superior to other known algorithms.

关键词： nearest neighbor pattern recognition hypersphere classification.

作者: Xiaopeng Yu Xiaogao Yu

作者单位: Computer School, Wuhan University, Wuhan, 430072 Hubei University of Economics, Wuhan, 430070

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 535-540

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）