## 3 Results  
### Comparison of Fixed Expression Weight Generation Function and Fixed Weight Ensemble Results  
The ensemble of (SVM + MLP), (KNN + MLP), and (RF + MLP) learners was applied to the battery charging detection data, fall detection data, and motion state recognition data, respectively, where SVM is the Support Vector Machine [27] model, MLP is the Multi-Layer Perceptron [28] model, KNN is the K-Nearest Neighbors [29] model, and RF is the Random Forest [30] model. The hyperparameters of each model are shown in Table 4. The Area Under the Curve (AUC) metrics were recorded for the single learner, three weight generation function ensembles, and fixed weight ensembles (the optimal weight distribution chosen from 1:9 to 9:1 for the fixed weight ensemble). The results are shown in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hyper Parameter** & **SVM** & **MLP** & **KNN** & **RF** \\ \hline C (Penalty coefficient) & 0.88 & * & * & * \\ \hline kernel & RBF & * & * & * \\ \hline gamma & \(\frac{1}{n\_features}\) & * & * & * \\ \hline random\_state & 42 & 1 & 0 & 42 \\ \hline solver & * & lbfgs & * & * \\ \hline learning\_rate & * & 0.00001 & * & * \\ \hline hidden\_layer\_sizes & * & (5, 2) & * & * \\ \hline max\_iter & * & 200 & * & * \\ \hline n\_neighbors & * & * & 200 & * \\ \hline n\_estimators & * & * & * & 3 \\ \hline n\_jobs & * & * & * & \(-1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental Results on Three Datasets. The ensemble results using the fully trained neural network-based weight generation function are shown in Table 6. It can be observed that the neural network-based weight generation function performs better when dealing with balanced datasets. Figure 8: Neural Network-Based Weight Generation Function on Motion Data. Figure 6: Neural Network-Based Weight Generation Function on Battery Data. Figure 7: Neural Network-Based Weight Generation Function on Fall Data. The comparative experiments on three datasets are shown in Tables 7-9. As can be seen in the results, either fixed function ensemble or neural network function ensemble has better effects and improvements than stacking, bagging, and AdaBoost methods. Therefore, func-bagging is superior with greater flexibility, higher base learner performance improvement, and better interpretability. However, it should be noted that func-bagging is a post-processing method, which is more separate from stacking, bagging, and AdaBoost in terms of program design (it is necessary to train and obtain the output scores of each base classifier separately before func-bagging can be performed). Therefore, if a unified training process is required, stacking and bagging will be more appropriate. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AUC** & \
\begin{tabular}{c} **Improvement over Base** \\ **Classifier** \\ \end{tabular} & **Note** \\ \hline MLP & 0.8168 & * & One of the base classifiers \\ \hline KNN & 0.8170 & * & One of the base classifiers \\ \hline Fixed Weight Ensemble & 0.8375 & 2.522\% &
\begin{tabular}{c} The weight ratio of the two \\ base classifiers is 4:6 \\ \end{tabular} \\ \hline Fixed Function Ensemble & **0.8407** & **2.913\%** & Sec function \\ \hline Neural Network Function & 0.8354 & 2.277\% & * \\ Ensemble & 0.8336 & 2.044\% & * \\ \hline Bagging (MLP) & 0.8345 & 2.167\% & * \\ \hline Bagging (KNN) & 0.8192 & 0.269\% & * \\ \hline Logistic & 0.8118 & * & Base classifier \\ \hline Adaboost (Logistic) & 0.8123 & 0.062\% & * \\ \hline Gaussian Naive Bayes & 0.8001 & * & Base classifier \\ \hline Adaboost (Gaussian & & & \\ Native Bayes) & 0.6538 & \(-18.285\%\) & * \\ \hline \hline \end{tabular}  
* The model itself is the base classifier or no notes. **bold:** The largest value in this column.  
\end{table} 
Table 8: Comparative Experiment on Fall Data.