An Improved KNN algorithm Based on Kernel Methods and Attribute Reduction
Among the classification algorithms in machine learning,the KNN (K nearest neighbor) algorithm is one of the most frequent used methods for its characteristics of simplicity and efficiency.Even though KNN algorithm is very effective in many situations while it still has two shortcomings, not only is the efficiency of this classification algorithm obviously affected by redundant dimensional features,but also the categorization accuracy is seriously influenced by the distribution of training samples.In this paper,we proposed a stepwise KNN algorithm based on kernel methods and attribute reduction which can effectively tackle with the problems above.We calculated the accuracy rate of the proposed algorithm and compared it with basic KNN algorithm in the experiments with use of four UCI datasets.The experiment results show that the stepwise KNN algorithm (denoted by SWKNN) performs better than the original KNN algorithm with the improvement of average 13.8% in accuracy.
KNN stepwise kernel methods attribute reduction
Wang Xueli Jiang Zhiyong Yu Dahai
School of Science Beijing University of Posts and Telecommunications Beijing, China
国际会议
秦皇岛
英文
567-570
2015-09-18（万方平台首次上网日期，不代表论文的发表时间）