Enhancement of K-nearest neighbor algorithm based on weighted entropy of attribute value

• Published in: 2012 5th International Conference on Biomedical Engineering and Informatics pp. 1261 - 1264
• Main Authors: Xingjiang Xiao, Huafeng Ding
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: cross validation; K nearest neighbor; weighted entropy
• ISBN: 9781467311830; 1467311839
• DOI: 10.1109/BMEI.2012.6513101

The traditional K-nearest neighbor algorithm usually adopts Euclidean distance formula to measure the distance between two samples. Since each attribute functions differently in the actual sample data collection, the accuracy of the classification will be reduced consequently. In order to improve traditional KNN and KNN with weighted distance which is on the distance definition and test mode, this article proposes one method to measure the attribute value and entropy weight, namely K-nearest neighbor algorithm based on weighted entropy of attribute value. The experiment indicated that, compared with the traditional K-nearest neighbor algorithm, the algorithm proposed in this article can not only guarantee the efficiency of classification but also enhance the accuracy of classification.