NDLI: An Effective Method To Improve kNN Text Classifier

Content Provider	IEEE Xplore Digital Library
Author	Xiulan Hao Xiaopeng Tao Chenghong Zhang Yunfa Hu
Copyright Year	2007
Description	Author affiliation: Fudan Univ., Shanghai (Xiulan Hao; Xiaopeng Tao)
Abstract	Many of standard classification algorithms usually assume that the training examples are evenly distributed among different classes. However, unbalanced data sets often appear in many applications. As a simple, effective categorization method, kNN is widely used, but it suffers from biased data sets, too. In developing the Prototype of Internet Information Security for Shanghai Council of Information and Security, we detect that when training data set is biased, almost all test documents of some rare categories are classified into common ones. To alleviate such a misfortune, we propose a novel concept, critical point (CP), and adapt traditional kNN by integrating CP's approximate value, LB or UB, training number with decision rules. Exhaustive experiments illustrate that the adapted kNN achieves significant classification performance improvement on biased corpora.
Starting Page	379
Ending Page	384
File Size	406218
Page Count	6
File Format	PDF
ISBN	9780769529097
DOI	10.1109/SNPD.2007.296
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Concurrent computing Text categorization Information security Computer networks Internet Distributed computing Management training Artificial intelligence Testing Software engineering
Content Type	Text
Resource Type	Article

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