NDLI: Comparison of attribute selection techniques and algorithms in classifying bad behaviors of vocational education students

Content Provider	IEEE Xplore Digital Library
Author	Wongpun, S. Srivihok, A.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Kasetsart Univ., Bangkok (Wongpun, S.; Srivihok, A.)
Abstract	This study presents the comparison of attribute selection techniques which used for classifying the bad behaviors of vocational education students. There are two classification methods: hybrid classification and single classification. Hybrid classification includes two steps, step one is attribute selection by search method using genetic search and results are compared by three evaluators: 1) Correlation-based Feature Selection (CFS) 2) Consistency-based Subset Evaluation and 3) Wrapper Subset Evaluation. Step two is the classification of data set by using selected attributed from step one and four classification algorithms. Next, Simple classification used classification algorithms only without attribute selection. The four classification algorithms that used in this experiment for comparing in two methods are : 1) Naive Bayes classifier 2) Baysian Belief Network 3) C4.5 algorithm and 4) RIPPER algorithm. The measurements of classification efficiency had been obtained by using the k-fold cross validation technique. From the experiment, it was found that hybrid classification technique using genetic search and CFS evaluator with C4.5 algorithm, gives the highest accuracy rate at 82.52%. However, results from F-measure evaluation showed that C4.5 algorithm did not fit for all data types. The hybrid classification technique using genetic search and wrapper subset with Baysian belief network can give a better precision value which can be seen in the F-measure, and it gives the accuracy rate at 82.42%.
Starting Page	526
Ending Page	531
File Size	316856
Page Count	6
File Format	PDF
ISBN	9781424414895
DOI	10.1109/DEST.2008.4635213
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-02-26
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Accuracy Baysian belief network Education Genetics C4.5 Algorithm Attribute selection Classification algorithms Data mining RIPPER Algorithm Naive Bayes classifier Gain
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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