A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data

Der Chiang Li, Szu Chou Chen, Yao San Lin, Wen Yen Hsu

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)


The problem of imbalanced data has a heavy impact on the performance of learning models. In the case of an imbalanced text dataset, minority class data are often classified to the majority class, resulting in a loss of minority information and low accuracy. Thus, it is a serious challenge to determine how to tackle the high imbalance ratio distribution of datasets. Here, we propose a novel classification method for learning tasks with imbalanced test data. It aims to construct a method for data preprocessing that researchers can apply to their learning tasks with imbalanced text data and save the efforts to search for more dedicated learning tools. In our proposed method, there are two core stages. In stage one, balanced datasets are generated using an asymmetric cost-sensitive support vector machine; in stage two, the balanced dataset is classified using the symmetric cost-sensitive support vector machine. In addition, the learning parameters in both stages are adjusted with a genetic algorithm to create an optimal model. A Yelp review dataset was used to validate the effectiveness of the proposed method. The experimental results showed that the proposed method led to a better performance subject to the targeted dataset, with at least 75% accuracy, and revealed that this new method significantly improved the learning approach.

出版狀態Published - 2022 3月

All Science Journal Classification (ASJC) codes

  • 電腦科學(雜項)
  • 化學(雜項)
  • 一般數學
  • 物理與天文學(雜項)


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