Abstract: Random forest is a commonly used classification method in the field of data mining and machine learning,which has become a research focus of scholars at home and abroad,and has been widely applied to various practical problems.The traditional random forest methods do not consider the influence of the number of classes on the classification effect,and neglect the correlation between base classifiers and classes,limiting the performance of the random forest in dealing with multi-class classification problems.In order to solve the problem better,combined with the characteristics of multi-class classification problem,this paper proposed a randomization of classes based random forest algorithm (RCRF).From the perspective of classes,the randomization of classes is added on the basis of two kinds of traditional randomizations of random forest,and the corresponding base classifiers with different emphasis are designed for diffe-rent classes.The structures of the decision tree generated by the base classifier are different because different classifiers focus on different classes,which can not only guarantee the performance of the single base classifier,but also further increase the diversity of base classifier.In order to verify the validity of the proposed algorithm,RCRF is compared with other algorithms on 21 data sets in UCI database.The experiment is carried out from two aspects.On the one hand,the accuracy,F1-measure and Kappa coefficient are used to verify the performance of RCRF algorithm.On the other hand,the κ-error diagram is used to compare and analyze various algorithms from the perspective of diversity.Experimental results show that the proposed algorithm can effectively improve the overall performance of the integrated model and has obvious advantages in dealing with multi-class classification problems.

Key words: Diversity, Multi-class classification problems, Random forest, Randomization of classes