Collaboration-training for noise filtering based on genetic algorithm
Guo Ta · Jisuanji gongcheng yu sheji · 2014
To decrease the introduction of noise data during the period of classifier training and to increase the ability of the classification when unlabeled data is used to update classifier,a collaboration-training based on genetic algorithm for noise filtering is proposed(CGA).Based on the optimization function of genetic algorithm,the procedure of CGA for assisting collaborative training to choose valuable data is presented.Experiments on UCI datasets prove that the algorithm is benefit for updating classifier and efficient for preventing the introduction of noise data.