A New Multi-classifier Ensemble Algorithm Based on D-S Evidence Theory

Classifier ensemble is an important research content of ensemble learning, which combines several base classifiers to achieve better performance. However, the ensemble strategy always brings difficulties to integrate multiple classifiers. To address this issue, this paper proposes a multi-classifier ensemble algorithm based on D-S evidence theory. The principle of the proposed algorithm adheres to two primary aspects. (a) Four probability classifiers are developed to provide redundant and complementary decision information, which is regarded as independent evidence. (b) The distinguishing fusion strategy based on D-S evidence theory is proposed to combine the evidence of multiple classifiers to avoid the mis-classification caused by conflicting evidence. The performance of the proposed algorithm has been tested on eight different public datasets, and the results show higher performance than other methods.