Classifying unstructured text using structured training instances and ensemble classifiers

Andreas Lianos, Yanyan Yang

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    Typical supervised classification techniques require training instances similar to the values that need to be classified. This research proposes a methodology that can utilize training instances found in a different format. The benefit of this approach is that it allows the use of traditional classification techniques, without the need to hand-tag training instances if the information exists in other data sources. The proposed approach is presented through a practical classification application. The evaluation results show that the approach is viable, and that the segmentation of classifiers can greatly improve accuracy.
    Original languageEnglish
    Pages (from-to)58-73
    JournalJournal of Intelligent Learning Systems and Applications
    Issue number2
    Publication statusPublished - 26 May 2015


    • Ensemble Classification
    • Diversity
    • Training Data


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