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GENERALIZED ONE-CLASS SUPPORT VECTOR MACHINES WITH JOINTLY OPTIMIZED HYPERPARAMETERS THEREOF
GENERALIZED ONE-CLASS SUPPORT VECTOR MACHINES WITH JOINTLY OPTIMIZED HYPERPARAMETERS THEREOF
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机译：整体优化超参数的广义一类支持向量机
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摘要
Absence of well-represented training datasets cause a class imbalance problem in one-class support vector machines (OC-SVMs). The present disclosure addresses this challenge by computing optimal hyperparameters of the OC-SVM based on imbalanced training sets wherein one of the class examples outnumbers the other class examples. The hyperparameters kernel co-efficient y and rejection rate hyperparameter v of the OC-SVM are optimized to trade-off the maximization of classification performance while maintaining stability thereby ensuring that the optimized hyperparameters are not transient and provide a smooth non-linear decision boundary to reduce misclassification as known in the art. This finds application particularly in clinical decision making such as detecting cardiac abnormality condition under practical conditions of contaminated inputs and scarcity of well-represented training datasets.
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