## 6 Conclusions  
CBA-CLSVE is a Class-Level Soft-Voting Ensemble for intrusion detection. In ensemble learning, Support Vector Machine, K-Nearest Neighbor, and Decision Tree are combined to construct an ensemble model with better generalizability, robustness and prediction performance. To improve the global convergence of the population, a CBA based on a tent chaotic map is proposed to generate the class-level weight. A weighted fitness function combining the Detection Rate and the False Positive Rate is defined in the CBA. The hard-voting and soft-voting methods are used to combine results from different base learners. The CBA-based voting methods with weights (whether hard voting or soft voting) are designed. The performance of different voting ensemble methods with or without feature selection are compared. Finally, to compare with the CBA-based method, the BA and the GA approaches are also used to combine the expert opinions. The approaches mentioned above were empirically compared using the NSL-KDD, UNSW-NB15 and CICIDS2017 datasets. The experimental results show that the class-level weights optimized by the CBA can be used to improve the combinative performance and the same performance of the ensemble can be obtained with half of the total number of features or fewer. It also shows that the CBA method has better results in searching performance and efficiency than the basic BA and GA methods. In this paper, the heuristic algorithm is used to integrate the base learners, which can improve the ensemble performance. Therefore, our future work will involve improving current heuristic algorithms or proposing new intelligent heuristic algorithms. In addition, studying other methods for integrating the base learners will be the direction of our efforts. Methodology, Y.S. ; validation, Y.S., K.Z., Y.Y., S.L. and M.H. ; investigation, Y.S. and Y.Y. ; resources, Y.S. and S.L. ; data curation, Y.S. ; writing--original draft preparation, Y.S. ; writing--review and editing, Y.S. ; visualization, Y.S. ; supervision, K.Z. and M.H. ; project administration, M.H. ; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Fundamental Research Funds for the Central Universities, grant number ZY20215151, the Earthquake Science and Technology Spark Program Youth Project Research, grant number XH21057Y and the Natural Science Project of Xinjiang University Scientific Research Program, grant number XJEDU2021Y003. Not applicable. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Feature** & **Classification** & \multirow{2}{*}{**\#Features**} & \multirow{2}{*}{**DR (\%)**} & \multirow{2}{*}{**FPR (\%)**} & \multirow{2}{*}{**Acc (\%)**} & \multirow{2}{*}{**F1 (\%)**} \\  & **Selection** & & & & & & \\ \hline XGBoost [48] & N/A & DT & 42 & 98.75 & 17.16 & 95.25 & 97.01 \\ Naive bayes [49] & N/A & NB & 42 & 98.94 & 58.06 & 80.03 & 86.88 \\ MLPClassifier [50] & N/A & MLP & 42 & 99.41 & 21.43 & 94.82 & 96.77 \\ Random Forest [51] & N/A & DT & 42 & 99.35 & 18.73 & 95.50 & 97.20 \\ Extra-free ensemble [52] & N/A & Extra-tree & 42 & 97.98 & 16.69 & 94.78 & 96.70 \\ \multirow{2}{*}{Hete-ensemble [53]} & Spearman’s & LR, DT, Gradient- & & & & & \\  & rank & Boosting & 23 & 99.02 & 18.97 & 94.87 & 96.75 \\ CBA-CLSVE & Chi square & SVM, KNN, DT & 19 & 99.42 & 21.02 & 95.17 & 97.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison results with other approaches for intrusion detection on the UNSW-NB15 dataset.