RETRACTED ARTICLE: A novel PCA-DC-Bagging algorithm on yield stress prediction of RAFM steel

For most regression tasks, we often use an ensemble learning technology of Bagging algorithm. However, the traditional Bagging algorithm is susceptible to extreme values. This leads to high bias and high variance in the prediction process. Therefore, this paper proposes an improved Bagging algorithm based on the best decision Committee model and the idea of selecting the base learner, and we have presented the idea of using the decision-making committee to filter learner, train the decision-making committee by the base learner to classify the error on the test set. Using the optimal interval separation factor’s mathematical model which is derived by the Lagrange multiplier method to classify the evaluation levels. The decision committee is trained according to the assigned evaluation level, and the learner is selected and assembled according to the decision result of the decision committee members. Meanwhile, our theoretical analysis shows that there are two different cases, which we can use maximum likelihood estimation and stochastic process theory to build mathematical models for analysis. The analysis results based on reduced activated ferritic/martensitic (RAFM) steel data sets show that the proposed algorithm can be applied to data sets with high dimension, high redundancy, high contradictory samples, sparse data sets, and then, we gives the strict theoretical framework to guarantees the further development and promotion. This gives algorithm model.