An adaptive fuzzy kNN text classifier

Authors:
Wenqian Shang;Houkuan Huang;Haibin Zhu;Yongmin Lin;Youli Qu;Hongbin Dong
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:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Year:
2006

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Abstract

In recent years, kNN algorithm is paid attention by many researchers and is proved one of the best text categorization algorithms. Text categorization is according to training set which is assigned class label to decide a new document which is not assigned class label belongs to some kind of document. Until now, kNN algorithm has still some issues to need to study further. Such as: improvement of decision rule; selection of k value; selection of dimensions (i.e. feature set selection); problems of multiclass text categorization; the algorithm’s executive efficiency (time and space) etc. In this paper, we mainly focus on improvement of decision rule and dimension selection. We design an adaptive fuzzy kNN text classifier. Here the adaptive indicate the adaptive of dimension selection. The experiment results show that our algorithm is effective and feasible.