Improved Piecewise Nonlinear Combinatorial Adaboost Algorithm Based on Noise Self-detection
摘要
As traditional Adaboost algorithm is sensitive to noisy sample and the linear combination of base classifiers is irrational,a piecewise nonlinear Adaboost algorithm based on noise self-detection called NDK Adaboost is proposed.NDK Adaboost,drawing on traditional Adaboost algorithm whose error rate in training set decreases with iteration times exponentially,establishes directly a noise detection model to recognize noise,and maps the prediction samples to the relative positions of the training set.According to the neighbor samples’ distribution,it determines the weight of the base classifier.A higher classification accuracy rate of the algorithm can be drawn out among the different sample distribution.Experimental results show that,compared with the traditional Adaboost algorithm and the related improved algorithms,NDK Adaboost has a higher classification accuracy rate.