Compensating Hypothesis by Negative Data

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 mining negative data based on the understanding of Chinese traditional Yin-Yang theory.