Multi-label rules algorithm based associative classification

Neda Abdelhamid, Aladdin Ayesh, Wael Hadi

    Research output: Contribution to journalArticlepeer-review

    17 Citations (Scopus)


    Current associative classification (AC) algorithms generate only the most obvious class linked with a rule in the training data set and ignore all other classes. We handle this problem by proposing a learning algorithm based on AC called Multi-label Classifiers based Associative Classification (MCAC) that learns rules associated with multiple classes from single label data. MCAC algorithm extracts classifiers from the whole training data set discovering all possible classes connected with a rule as long as they have sufficient training data representation. Another distinguishing feature of the MCAC algorithm is the classifier building method that cuts down the number of rules treating one known problem in AC mining which is the exponential growth of rules. Experimentations using real application data related to a complex scheduling problem known as the trainer timetabling problem reveal that MCAC's predictive accuracy is highly competitive if contrasted with known AC algorithms.

    Original languageEnglish
    Article number1450001
    JournalParallel Processing Letters
    Issue number1
    Publication statusPublished - 31 Mar 2014


    • Classification
    • Data Mining
    • Multiple label rules
    • Parallel Rule Generation


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