NDLI: New Classification Algorithms for Developing Online Program Recommendation Systems

Content Provider	IEEE Xplore Digital Library
Author	Meller, T. Wang, E. Fuhua Lin Chunsheng Yang
Copyright Year	2009
Abstract	This paper presents two novel nearest-neighbor-like classification algorithms for program recommendation in a Web-based system, which provides a program planning service to academic advisors and students of post-secondary institutions. To evaluate the accuracy of classification for program recommendations generated by our algorithm, a statistical study was conducted through comparing our algorithm against two well-known classification algorithms, the Naïve Bayes algorithm and the J48 algorithm, for making recommendations to students based on their academic history. The study shows that our proposed nearest-neighbor-like algorithms outperform the two well-known classification algorithms in terms of student classification success rate when there is uncertainty present in the data.
Starting Page	67
Ending Page	72
File Size	268047
Page Count	6
File Format	PDF
ISBN	9781424433612
DOI	10.1109/eLmL.2009.19
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-02-01
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Educational institutions Inference algorithms Classification algorithms History Data mining Technology planning Information technology Mobile computing Nearest neighbor searches
Content Type	Text
Resource Type	Article

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