## 3 HDELC Algorithm
### Classifier Selection and Ensemble Models
#### 3.2.2 Classifier Ensemble Process  
Building a diverse set of base classifiers is critical for dealing with the problem of classifying high-dimensional data. In general, inconsistency in base classifier predictions increases the accuracy of ensemble learning without affecting the individual error rate [31]. According to the Error-Divergence Decomposition Theory framework [32], the generalization error for the ensemble learning model can be decomposed into three main components: the average error (i.e., bias) of base learners, the variance among base learners, and the covariance (reflecting the correlation) among base learners, which is simplified as follows:  
\[E=bias^{2}+Variance+Noise \tag{24}\]  
where bias represents the mean error, which reflects the difference between the mean and the actual value predicted using the ensemble model; Variance is the inter-base learner disagreement, which measures the difference between the base learner's predictions; and Noise represents the noise, which reacts to the random errors in the data themselves. The framework explains the rationale for assigning non-overlapping feature subspaces to classifiers and facilitates the understanding of how the generalization ability of the ensemble learning model is affected by the errors of its constituent learners, the diversity (divergence) among learners, and the correlation among learners. 1. In ensemble learning, enhancing inter-model diversity is considered a critical factor in improving the generalization ability of integration. By endowing each classifier with specific, non-overlapping feature subspaces, parsing of the dataset from different dimensions can be achieved, thus enabling the integrated model to incorporate this multi-dimensional information, and significantly improving the model's overall performance. 2. The low correlation between base learners is decisive for minimizing the overall error of the ensemble learning model. Decorrelation of the error prediction can be achieved by assigning different feature subsets to different classifiers, and this strategy naturally reduces the dependency between base learners, thus enhancing the robustness of the integrated model. 3. Assigning non-overlapping feature subspaces to classifiers may increase the bias of individual classifiers. However, by integrating these classifiers with high levels of diversity, the variance of the overall model can be reduced. In essence, this approach utilizes the ability of integrated learning to reduce variance while keeping bias as constant as possible. Therefore, the HDELC algorithm expects that multiple classifiers can increase the diversity among base classifiers, and the output of the first layer is fused using the second layer learning algorithm, whose effectiveness depends not only on the accuracy of individual learners but also on diversity among learners. In order to reduce the model load and complexity, base learners in the classifier integration model in this paper are traditional machine learning algorithms: decision tree, random forest, Adaboost, gradient boosting, extra trees, and SVM. Based on the classifier selection, the Stacking ensemble method is improved by assigning the reconstructed set of feature subspaces to different learners. The core of the CSE algorithm lies in its dynamic allocation strategy of feature subspaces, which considers the sensitivity of each classifier in integrating different data features. The feature space after PCA dimensionality reduction is partitioned into several feature subspaces. 1. First, the algorithm picks an initial feature subspace for the first classifier of integration. Then, it allocates mutually exclusive disjoint feature subspaces for each subsequent classifier in turn (classifier) until all the preset classifiers have been configured. All classifiers are level 1 classifiers, and each base learner is given a prediction. This approach enables the complementary contribution of information, which in turn enhances the ability of the overall integrated model to capture data diversity. 2.