Fuzzy Decision Rule Construction Using Fuzzy Decision Trees: Application to E-Learning Database

Fuzzy Decision Rule Construction Using Fuzzy Decision Trees: Application to E-Learning Database

Malcolm J. Beynon, Paul Jones
ISBN13: 9781605668147|ISBN10: 1605668141|EISBN13: 9781605668154
DOI: 10.4018/978-1-60566-814-7.ch007
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MLA

Beynon, Malcolm J., and Paul Jones. "Fuzzy Decision Rule Construction Using Fuzzy Decision Trees: Application to E-Learning Database." Soft Computing Applications for Database Technologies: Techniques and Issues, edited by K. Anbumani and R. Nedunchezhian, IGI Global, 2010, pp. 104-124. https://doi.org/10.4018/978-1-60566-814-7.ch007

APA

Beynon, M. J. & Jones, P. (2010). Fuzzy Decision Rule Construction Using Fuzzy Decision Trees: Application to E-Learning Database. In K. Anbumani & R. Nedunchezhian (Eds.), Soft Computing Applications for Database Technologies: Techniques and Issues (pp. 104-124). IGI Global. https://doi.org/10.4018/978-1-60566-814-7.ch007

Chicago

Beynon, Malcolm J., and Paul Jones. "Fuzzy Decision Rule Construction Using Fuzzy Decision Trees: Application to E-Learning Database." In Soft Computing Applications for Database Technologies: Techniques and Issues, edited by K. Anbumani and R. Nedunchezhian, 104-124. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-814-7.ch007

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Abstract

This chapter considers the soft computing approach called fuzzy decision trees (FDT), a form of classification analysis. The consideration of decision tree analysis in a fuzzy environment brings further interpretability and readability to the constructed ‘if .. then ..’ decision rules. Two sets of FDT analyses are presented, the first on a small example data set, offering a tutorial on the rudiments of one FDT technique. The second FDT analysis considers the investigation of an e-learning database, and the elucidation of the relationship between weekly online activity of students and their final mark on a university course module. Emphasis throughout the chapter is on the visualization of results, including the fuzzification of weekly online activity levels of students and overall performance.

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