Dynamic combination of multiple classifiers based on central similarity

According to the specific characteristics of samples, dynamic classifier ensemble chooses appropriate classifier for decision-making, which improve classification accuracy effectively, but increase the cost of running time. Therefore, Dynamic Combination of Multiple Classifiers Based on Central Similarity is proposed in this paper, which chooses different members classifier according to the similarity between classification samples and each class center to avoid validation process of neighborhood samples, and at the same time, adjust each corresponding weights to improve accuracy furthermore. The experiments demonstrate that this algorithm reduces the running time as well as improve the accuracy of integration classification, besides, choice of classifiers don't depend on neighborhood samples any more, so it shows a higher accuracy of classification for small scale sample training set.