Cost-Sensitive Ensemble Classification Algorithm For Medical Image

Medical image classification is an important part in domain-specific application image mining. In this paper, we quantify the domain knowledge about medical image for feature extraction. We propose a cost-sensitive ensemble classification (CEC) algorithm which uses a new training method and adopts a new method to acquire parameters. In the weak classifier training process, we mark the samples that are wrongly classified in the former iteration, use the method of re-sampling in the samples that are correctly classified, and put all the wrongly classified samples in the next training. The classification can pay more attention to those samples that are hardly classified. The weight parameters of weak classifiers are determined not only by the error rates, but also by their abilities to recognise the positive samples. Experimental results show that our algorithm is more efficient for medical image classification.