An Adaptive Fuzzy kNN Text Classifier Based on Gini Index Weight

Authors:
Wenqian Shang;Youli Qu;Haibin Zhu;Houkuan Huang;Yongmin Lin;Hongbin Dong
Affiliations:
Beijing Jiaotong University, China;Beijing Jiaotong University, China;Nipissing University, Canada;Beijing Jiaotong University, China;Beijing Jiaotong University, China;Beijing Jiaotong University, China
Venue:
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
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. But for a classifier, text preprocessing is the bottleneck of categorization. In the original feature space, there are always thousands upon thousands words. The dimension of feature space is very high. So in this paper, we adopt a new feature weight method---- improved Gini index to reduce the dimension of feature space and improve the categorization precision. In addition, we discuss the 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.