APJIS Asia Pacific Journal of Information Systems

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The Journal for Information Professionals

Asia Pacific Journal of Information Systems (APJIS), a Scopus and ABDC indexed journal, is a
flagship journal of the information systems (IS) field in the Asia Pacific region.

ISSN 2288-5404 (Print) / ISSN 2288-6818 (Online)

Editor : Hee-Woong Kim

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Current Issue

Date December 2019
Vol. No. Vol. 29 No. 4
DOI https://doi.org/10.14329/apjis.2019.29.4.789
Page 789~816
Title Text Classification with Heterogeneous Data Using Multiple Self-Training Classifiers
Author William Xiu Shun Wong, Donghoon Lee, Namgyu Kim
Keyword Text Mining, Text Classification, Heterogeneity Learning, Semi-Supervised Learning, Ensemble Learning
Abstract Text classification is a challenging task, especially when dealing with a huge amount of text data. The performance of a classification model can be varied depending on what type of words contained in the document corpus and what type of features generated for classification. Aside from proposing a new modified version of the existing algorithm or creating a new algorithm, we attempt to modify the use of data. The classifier performance is usually affected by the quality of learning data as the classifier is built based on these training data. We assume that the data from different domains might have different characteristics of noise, which can be utilized in the process of learning the classifier. Therefore, we attempt to enhance the robustness of the classifier by injecting the heterogeneous data artificially into the learning process in order to improve the classification accuracy. Semi-supervised approach was applied for utilizing the heterogeneous data in the process of learning the document classifier. However, the performance of document classifier might be degraded by the unlabeled data. Therefore, we further proposed an algorithm to extract only the documents that contribute to the accuracy improvement of the classifier.


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