A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data
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 determi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/3/567 |
| _version_ | 1797441538232418304 |
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| author | Der-Chiang Li Szu-Chou Chen Yao-San Lin Wen-Yen Hsu |
| author_facet | Der-Chiang Li Szu-Chou Chen Yao-San Lin Wen-Yen Hsu |
| author_sort | Der-Chiang Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T12:24:36Z |
| format | Article |
| id | doaj.art-333520de20df45d59e04bfad3ab22b0b |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T12:24:36Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-333520de20df45d59e04bfad3ab22b0b2023-11-30T22:36:25ZengMDPI AGSymmetry2073-89942022-03-0114356710.3390/sym14030567A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text DataDer-Chiang Li0Szu-Chou Chen1Yao-San Lin2Wen-Yen Hsu3Department of Industrial and Information Management, National Cheng Kung University, Tainan City 70101, TaiwanInstitute of Information Management, National Cheng Kung University, Tainan City 70101, TaiwanSingapore Centre for Chinese Language, Nanyang Technological University, Singapore 279623, SingaporeInstitute of Information Management, National Cheng Kung University, Tainan City 70101, TaiwanThe 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.https://www.mdpi.com/2073-8994/14/3/567imbalanced datasentiment analysistext miningsupport vector machine |
| spellingShingle | Der-Chiang Li Szu-Chou Chen Yao-San Lin Wen-Yen Hsu A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data Symmetry imbalanced data sentiment analysis text mining support vector machine |
| title | A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data |
| title_full | A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data |
| title_fullStr | A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data |
| title_full_unstemmed | A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data |
| title_short | A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data |
| title_sort | novel classification method based on a two phase technique for learning imbalanced text data |
| topic | imbalanced data sentiment analysis text mining support vector machine |
| url | https://www.mdpi.com/2073-8994/14/3/567 |
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