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- W632636698 abstract "AbstractThe performance of machine learning algorithms is affected by the imbalanced distribution of data among classes. This issue is crucial in various practical problem domains, for example, in medical diagnosis, network intrusion, fraud detection etc. Most efforts so far are mainly focused upon binary class imbalance problem. However, the class imbalance problem is also reported in multi-class scenario. The solutions proposed by the researchers for two-class scenario are not applicable to multi-class domains. So, in this paper, we have developed an effective Weighted Multi-class Least Squares Twin Support Vector Machine (WMLSTSVM) approach to address the problem of imbalanced data classification for multi class. This research work employs appropriate weight setting in loss function, e.g. it adjusts the cost of error for imbalanced data in order to control the sensitivity of the classifier. In order to prove the validity of the proposed approach, the experiment has been performed on fifteen benchmark d..." @default.
- W632636698 created "2016-06-24" @default.
- W632636698 creator A5004401828 @default.
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- W632636698 date "2015-01-01" @default.
- W632636698 modified "2023-10-13" @default.
- W632636698 title "An effective Weighted Multi-class Least Squares Twin Support Vector Machine for Imbalanced data classification" @default.
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- W632636698 doi "https://doi.org/10.1080/18756891.2015.1061395" @default.
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