Weighted Least Square Twin Support Vector Machine for Imbalanced Dataset

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Provided by: Science & Engineering Research Support soCiety (SERSC)
Topic: Big Data
Format: PDF
In this paper the authors propose a Weighted Least Square Twin Support Vector Machine (WLSTSVM) for imbalanced dataset. Real world data are imbalanced in nature due to which most of the classification techniques do not work well. In imbalanced data, there is a huge difference between the numbers of data samples of classes. One class data samples are larger as compared to other class data samples. This paper discusses the traditional methods of handling imbalanced data and proposes an improvement over least square twin support vector machine.
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