NDLI: Improved classification based on predictive association rules

Content Provider	IEEE Xplore Digital Library
Author	Zhixin Hao Xuan Wang Lin Yao Yaoyun Zhang
Copyright Year	2009
Description	Author affiliation: Intelligence Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China (Zhixin Hao; Xuan Wang; Lin Yao; Yaoyun Zhang)
Abstract	Classification based on predictive association rules (CPAR) is a kind of association classification methods which combines the advantages of both associative classification and traditional rule-based classification. For rule generation, CPAR is more efficient than traditional rule-based classification because much repeated calculation is avoided and multiple literals can be selected to generate multiple rules simultaneously. Despite these advantages above in rule generation, the prediction processes have the weaknesses of class rule distribution imbalance and interruption of incorrect class rules. Further, it is useless to instances satisfying no rules. To tackle these problems, this paper presents Class Weighting Adjustment, Center Vector-based Pre-classification and Post-processing with Support Vector Machine. Experiments on Chinese text classification corpus TanCorp show that our algorithm achieves an average improvement of 5.91% on F1 score compared with CPAR.
Starting Page	1165
Ending Page	1170
File Size	188933
Page Count	6
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5345954
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Association rules Support vector machines Support vector machine classification Data mining Classification algorithms Cybernetics USA Councils Text categorization Testing Prediction algorithms Support Vector Machine CPAR Class Weighting Adjustment Center Vector-based Pre-classification
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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