Imbalanced Data Problem of Relevance Vector Machine Customer Identification

Imbalanced data problem has a significant impact on the performance of RVM pattern recognition. Customer identification is an important application domain of pattern recognition which is mapping from samples to different categories by machine learning. In order to solve the problem, the paper proposes a method named up-sampling which overcomes the phenomenon that the machine is more partial to the majority classes while ignoring the sparse and decreases the false judgment about the sparse ones.