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    An evaluation study on text categorization using automatically generated labeled dataset

    Access Status
    Fulltext not available
    Authors
    Zhu, Dengya
    Wong, K.
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Zhu, D. and Wong, K. 2017. An evaluation study on text categorization using automatically generated labeled dataset. Neurocomputing. 249: pp. 321-336.
    Source Title
    Neurocomputing
    DOI
    10.1016/j.neucom.2016.04.072
    ISSN
    0925-2312
    School
    School of Information Systems
    URI
    http://hdl.handle.net/20.500.11937/53578
    Collection
    • Curtin Research Publications
    Abstract

    Naïve Bayes, k-nearest neighbors, Adaboost, support vector machines and neural networks are five among others commonly used text classifiers. Evaluation of these classifiers involves a variety of factors to be considered including benchmark used, feature selections, parameter settings of algorithms, and the measurement criteria employed. Researchers have demonstrated that some algorithms outperform others on some corpus, however, inconsistency of human labeling and high dimensionality of feature spaces are two issues to be addressed in text categorization. This paper focuses on evaluating the five commonly used text classifiers by using an automatically generated text document collection which is labeled by a group of experts to alleviate subjectivity of human category assignments, and at the same time to examine the influence of the number of features on the performance of the algorithms.

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