## 1 Introduction
However, the integration method of obtaining the prediction averages of multiple algorithm models or different parameter models of the same type of algorithm cannot reflect the differences in data observations of different prediction algorithms. Each algorithm cannot exert its own advantages to compensate for its own errors. This combination method does not have sufficient theoretical support, and the principle is relatively thin [15]. Common ensemble learning methods include parallelized ensemble bagging, serialized ensemble boosting, and multilayer classifier blending and stacking [16; 17; 18]. Blending and stacking use a high-level metamodel to synthesize the output features of a low-level base model to enhance generalization ability and obtain higher prediction accuracy. [PERSON] et al. used random forest, adaptive booster, gradient booster decision tree, and other five models as the base model, and back-propagation neural network as the metamodel to build a stacking ensemble learning model for secondhand housing price prediction [19]. [PERSON] et al. constructed four ensemble models using the stacking technique of ensemble learning, providing a high-performance prediction method for unbalanced rockburst prediction [20]. The above literature reflects the advantages of the stacking ensemble learning model in prediction. The prediction performance of the stacking model is better than that of a single machine learning model, and the model reduces the risk of falling into a local minimum. Some scholars choose base models in other fields by calculating error correlation. The stacking ensemble learning model constructed by this method has better prediction performance than random selection, which provides a new method for the selection of basic models. [PERSON] et al. used the Spearman correlation coefficient to select the base model and built a stacking ensemble model, which can provide higher prediction accuracy in the case of intermittency and volatility of wind power [21]. [PERSON] et al. used the Pearson correlation coefficient of the two-dimensional vector as the correlation index to select the base learner, which performed better than the stacking model of randomly selecting the base learner [15]. The abovementioned literature constructs a stacking ensemble learning model through the correlation selection base model, which effectively improves the prediction performance of the model. Therefore, in order to optimize the performance of the stacked ensemble model, the learning ability and correlation degree of each basic learner must be analyzed. In this study, MIC was used to improve the selection method of the basic model, which could effectively identify linear and nonlinear relationships and make the comprehensive results of the prediction model more robust and accurate. Sample size and feature dimension have certain influence on prediction. It must be considered that we are dealing with high-dimensional, small samples of data, of which only a small number of samples are available. Seven features and 103 samples produce a scene that is prone to overfitting. Some scholars have conducted research on this issue. [PERSON] et al. used the averaging-ensemble of randomly projected Fisher linear discriminant classifiers method to adopt a complex covariance regularization scheme, avoiding the partial variance decomposition method, and improving the prediction performance of high-dimensional, small samples [22]. [PERSON] et al. proposed a random 2D-CNN model, which is based on a collection of random learning blocks. The learning block composed of 2D convolutional layers, batch-normalization layers, and max-pooling layers reduces the possibility of overfitting. In terms of predicting [PERSON]'s disease (high-dimensional, small sample), it demonstrates the advantages of the model and its practical application value. Avoiding overfitting, this model can effectively obtain the best training effect in a limited sample [23]. The above documents effectively solve the problem of high-dimensional, small samples prone to overfitting.