An improved kNN algorithm – fuzzy kNN

Authors:
Wenqian Shang;Houkuan Huang;Haibin Zhu;Yongmin Lin;Zhihai Wang;Youli Qu
Affiliations:
School of Computer and Information Technology, Beijing Jiaotong University, China;School of Computer and Information Technology, Beijing Jiaotong University, China;Senior Member, IEEE, Dept. of Computer Science, Nipissing University, North Bay, ON, Canada;School of Computer and Information Technology, Beijing Jiaotong University, China;School of Computer and Information Technology, Beijing Jiaotong University, China;School of Computer and Information Technology, Beijing Jiaotong University, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

As a simple, effective and nonparametric classification method, kNN algorithm is widely used in text classification. However, there is an obvious problem: when the density of training data is uneven it may decrease the precision of classification if we only consider the sequence of first k nearest neighbors but do not consider the differences of distances. To solve this problem, we adopt the theory of fuzzy sets, constructing a new membership function based on document similarities. A comparison between the proposed method and other existing kNN methods is made by experiments. The experimental results show that the algorithm based on the theory of fuzzy sets (fkNN) can promote the precision and recall of text categorization to a certain degree.