Research on a Steel Leakage Prediction Model Based on One vs Rest Genetic Algorithm Optimization Decision Tree

Abstract

Aiming at the problem that the decision tree model of small sample training data is difficult to obtain a high prediction 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 genetic 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 algorithms can achieve an accuracy of 98.39% and a reporting rate of 100% for the optimization decision tree steel leakage prediction model based on a class to class genetic algorithm after only 10 iterations. Compared with traditional decision tree algorithms， this algorithm can achieve higher accuracy and better generalization in very few iterations.

Keywords

• genetic algorithm； decision tree； continuous casting； steel lakage prediction