KerMinSVM for imbalanced datasets with a case study on arabic comics classification

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• 作者：Ammar Nayal ^{axn01@aub.edu.lb} ; Hadi Jomaa ^{hsj04@aub.edu.lb} ; Mariette Awad ; ^{mariette.awad@aub.edu.lb}
• 关键词：Imbalance datasets ; Support vector machines ; Arabic comics analysis ; Natural language processing ; Supervised classification
• 刊名：Engineering Applications of Artificial Intelligence
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：59
• 期：Complete
• 页码：159-169
• 全文大小：1598 K
• 卷排序：59

文摘

Many studies have been performed to classify large-sized text documents using different classifiers, ranging from simple distance classifiers such as K-Nearest-Neighbor (KNN) to more advanced classifiers such as Support Vector Machines. Traditional approaches fail when a short text is encountered due to sparsity resulting from a limited number of words. Another common problem in text classification is class imbalance (CI). CI occurs when one class of the data contains most of the samples while the other class contains only a few. Standard classifiers, when applied to imbalanced data, result in high accuracy for the majority class and low accuracy for the minority one. We were motivated to propose a novel framework for classifying the content of Arabic comics; therefore, we propose KerMinSVM, a kernel extension of our previously proposed MinSVM coupled with a new dimensionality featuring a reduction scheme based on word root frequency ratios (WRFR). KerMinSVM was tested on multiple imbalanced benchmark datasets, and the results were verified using three measures: accuracy, F-measure, and statistical analysis. WRFR was applied to the manual construction of the Arabic comic text dataset to detect strong content in children's comic books. Test results revealed that our proposed framework outperformed most of the methods for imbalanced datasets and short text classification.