کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
553868 873550 2009 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
On strategies for imbalanced text classification using SVM: A comparative study
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
پیش نمایش صفحه اول مقاله
On strategies for imbalanced text classification using SVM: A comparative study
چکیده انگلیسی

Many real-world text classification tasks involve imbalanced training examples. The strategies proposed to address the imbalanced classification (e.g., resampling, instance weighting), however, have not been systematically evaluated in the text domain. In this paper, we conduct a comparative study on the effectiveness of these strategies in the context of imbalanced text classification using Support Vector Machines (SVM) classifier. SVM is the interest in this study for its good classification accuracy reported in many text classification tasks. We propose a taxonomy to organize all proposed strategies following the training and the test phases in text classification tasks. Based on the taxonomy, we survey the methods proposed to address the imbalanced classification. Among them, 10 commonly-used methods were evaluated in our experiments on three benchmark datasets, i.e., Reuters-21578, 20-Newsgroups, and WebKB. Using the area under the Precision–Recall Curve as the performance measure, our experimental results showed that the best decision surface was often learned by the standard SVM, not coupled with any of the proposed strategies. We believe such a negative finding will benefit both researchers and application developers in the area by focusing more on thresholding strategies.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Decision Support Systems - Volume 48, Issue 1, December 2009, Pages 191–201
نویسندگان
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