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The properties of training data set such as size, distribution and number of attributes significantly contribute to the generalization error of a learning machine. A data set not well-distributed is prone to lead to a model with partial overfitting. The approach proposed in this paper for the binary classification enhances the useful data information by(More)
The properties of training data set such as size, distribution and number of attributes significantly contribute to the generalization error of a learning machine. A not-well-distributed data set is prone to lead to a partial overfitting model. The two approaches proposed in this paper for the binary classification enhance the useful data information by(More)
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