## 2 Related Work
### Applications of Ensemble Learning  
As ensemble learning techniques continue to advance and mature, their application scenarios have gradually expanded to various fields, such as medical testing, security monitoring, and more. An increasing number of industries and enterprises are leveraging \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Year** & **Model** & **Dataset** & \
\begin{tabular}{c} **Learning** \\ **Paradigms** \\ \end{tabular} \\ \hline MTS, HMTS, & 2023 & Perceptron, LR, RF, SVM, MLP. & UCI Repository. & Supervised, semi-supervised. \\ DMIS [8] & 2000 & Randomization, bagging, and boosting. & UCI Repository. & Supervised. \\   algorithm C4.5 [12] & 2016 &
\begin{tabular}{c} Trace boosting, C4.5, \\ randomization, sparsity-aware \\ algorithm, weighted quantile \\ sketch. \\ \end{tabular} & Allstate, Higgs Boson, Yahoo! LTRC, Criteo. & Supervised. \\  & & & Allstate Insurance Claim, Flight Delay, LETOR, KDD10, KDD12. & Supervised. \\ Multistrategy & 2004 & Bagging, wagging, boosting, SASC. & UCI Repository. & Supervised. \\ EnsembleS3 VM [16] & 2018 & SVM, LapSVM, S3 VMs, S4 VMs. & UCI Repository, PolSAR. & Semi-supervised. \\  & & & UCI Repository, & Semi-supervised. \\ PSEL [17] & 2016 & Decision tree. & armstrong—2002—v1 & Supervised. \\  & & & and other 16 cancer & datasets. \\  & & & Heart disease dataset, & \\ LAbEL [18] & 2020 & SVM, random forest, naive & Breast cancer Wisconsin (Diagnostic) dataset, Yelp, Abalone dataset, Gender voice. & Supervised. \\ Our Study & 2025 & Gbt, LR, RF, SVM, MLP. & UCI Repository, musk, magazine. & Supervised. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Differences between existing studies and this study. predictive methods derived from ensemble learning principles to detect and analyze target objects. By integrating the advantages of multiple models, these methods have significantly improved detection accuracy and robustness, especially in target recognition and prediction tasks in complex environments. The widespread application of this technology has not only enhanced system reliability but also provided a more precise basis for decision support and risk management. [PERSON] et al. [19] applied ensemble learning to develop a classification model that accurately estimates slope stability. The model constructs an ensemble classifier through two primary techniques: parallel learning and sequential learning. By identifying and learning failure modes of slopes under different conditions, the model is able to assess slope safety, thereby improving the accuracy of disaster warnings. [PERSON] et al. [20] used ensemble learning to detect fake reviews, integrating random forest, bagging, and AdaBoost, with support vector machines (SVMs) and multilayer perceptrons (MLP) serving as weak classifiers with optimized hyperparameters. The research results indicate that after optimizing the hyperparameters, the accuracy of using MLP as a standalone classifier is 68.2%, while the accuracy of the classifier integrating MLP with AdaBoost reaches 77.3%. This demonstrates that ensemble learning methods can effectively classify fake reviews. The study used a self-collected dataset of fake restaurant reviews to train the classifiers. [PERSON] et al. [21] proposed an image denoising method based on ensemble learning. This method utilizes different types of image prior knowledge to construct an ensemble denoising model composed of two denoising bases with shrinkage transformations. Through a resampling scheme, multiple denoising bases are sequentially combined multiple times to construct an integrated image denoiser. After performance validation, it can be verified that the ensemble image denoiser operates effectively. [PERSON] et al. [22] investigated a multikernel ensemble learning method for software defect classification and prediction. By leveraging ensemble learning techniques, they developed a multikernel classifier that combines the advantages of multikernel learning and ensemble learning.