New classification algorithms for developing online program recommendation systems

Thomas Meller, Fuhua Lin, Eric Wang, Chunsheng Yang

    Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

    2 Citations (Scopus)

    Abstract

    This paper presents two novel nearestneighbor- 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 nearestneighbor- like algorithms outperform the two well-known classification algorithms in terms of student classification success rate when there is uncertainty present in the data.

    Original languageEnglish
    Title of host publicationProceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009
    Pages67-72
    Number of pages6
    DOIs
    Publication statusPublished - 2009
    EventInternational Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009 - Cancun, Mexico
    Duration: 1 Feb. 20097 Feb. 2009

    Publication series

    NameProceedings - International Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009

    Conference

    ConferenceInternational Conference on Mobile, Hybrid, and On-line Learning, eLmL 2009
    Country/TerritoryMexico
    CityCancun
    Period1/02/097/02/09

    Keywords

    • Classification algorithms
    • Data mining
    • Program planning
    • Recommendation systems

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