Weighted Least Square Twin Support Vector Machine for Imbalanced Dataset.

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Abstract:
This research work proposes 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. This research work has performed experiment on five benchmark UCI datasets using 10-fold cross validation method. The results of experiment show that the proposed technique performed well for imbalanced dataset and its accuracy is better as compared to other existing methods. This research work presents the formulation of proposed approach for both linear and non-linear data samples. Weighted Least Square Twin Support Vector Machine for Imbalanced Dataset (PDF…. Available from: https://www.researchgate.net/publication/262105302_Weighted_Least_Square_Twin_Support_Vector_Machine_for_Imbalanced_Dataset [accessed Mar 29 2018].