Abstract: Traditional Dynamic Ensemble Selection (DES) in ensemble learning needs to select individual classifiers for all the test samples. However, it leads to highly computational cost. Due to this issue, a new Semi Dynamic Ensemble Selection (SemiDES) strategy is proposed in this paper, which consists of two stages. Individual classifiers are selected for all the test samples in the first stage. In the second stage, the classifiers for each test sample are selected dynamically. The final result is obtained by integrating the output of the two stages. The experimental results on UCI data set demonstrate the proposed method can obtain a better classification performance. Moreover, SemiDES can reduce the computational cost greatly.

Key words:  , ensemble learning； selective ensemble； dynamic ensemble selection； classification