An Improving Noise Immunity Algorithm for Multiple Kernel Boosting Using Noise Detection and Kernel Selection Technique

Abstract

Abstract Focus on the problem that the Multiple Kernel Boosting(MKBoost) algorithm is sensitive to noise, a Multiple Kernel Boosting algorithm based on weight update and kernel selection is proposed. Firstly, the algorithm use the combined classification error rate of the previously selected classifier and the current classifier to be selected as the selection index of the kernel function in the weak classifier before the kernel of the base classifier is selected in each iteration; Secondly, in the weight update stage, a new weight update method is constructed by fusing the noise-detection and the average of weights in Multiple Kernel Boosting algorithm, which reduce the sensitivity to noise samples. Among the 8 of UCI data sets with varying levels of noise, the algorithm was compared with MKBoost-D1, MKBoost-D2, under the accuracy criteria, it performed better than traditional MKBoost algorithms. Experimental results show that the algorithm is able to effectively reduce the sensitivity of MKBoost to noise, and also has better robustness than traditional MKBoost algorithms.