Aiming at the problem that the decision tree model of small sample training data is difficult to obtain a high pre-diction accuracy rate on multi classification problems,this paper establishes a breakout prediction model based on one vs rest genetic algorithm to optimize the decision tree.By making full use of the global search ability and robustness of ge-netic algorithm,strengthening the search control and supervision of the optimization process,the accuracy of the model is improved.Combined with the continuous casting production data of a steel plant,the breakout prediction model of genetic algorithm optimization decision tree based on one kind of congruence method was tested.The tests shows that genetic algo-rithms can achieve an accuracy of 98.39％and a reporting rate of 100％for the optimization decision tree steel leakage pre-diction model based on a class to class genetic algorithm after only 10 iterations.Compared with traditional decision tree al-gorithms,this algorithm can achieve higher accuracy and better generalization in very few iterations.
维普
万方数据