Transfer estimation is a new parameter estimation method, which utilizes samples from not only the target distribution
but also related ones. It is based on traditional estimators and can be used to solve the problem of small sample
estimation. The key problem in transfer estimation is how to set the weighting coefficients when designing the transfer
estimators. In this paper, we will propose a new algorithm to solve this problem. First, we will propose a common rule.
Following the proposed rule, we can formulate the weighting coefficient setting problem as a constrained optimization
problem. We will introduce an alternating optimized method and get a new algorithm of transfer estimation. The
estimation of evolving class priors in data stream classification is a typical and important small sample estimation
problem. In this paper, we will apply the proposed transfer estimation algorithm to the class prior estimation.
Experiments on benchmark data sets will be performed, which show that the proposed algorithm can improve the
performance on both class prior estimation and the final data stream classification.