###### Abstract
The Bayesian rule, fuzzy integrals and evidential reasoning can be applied to merge the soft outputs [10]. However, with the increase of the data complexity, some flexible methods are needed as the combination methods of the ensemble, which can be adjusted according to the attributes of the dataset used. Some scholars indicate that the efficiency of combinatorial methods can be improved by assigning weights to the classifiers [11]. [PERSON] et al. [12] point out that it is necessary to assign weights to different base learners according to their performance in the heterogeneous learning. [PERSON] et al. [13] provide two weight-optimization methods under the framework of the Class-Level Soft-Voting Ensemble. They suggest that the class-specific soft-voting method can refine the weights from classifiers to classes and improve the combinative performance. In this paper, the Class-Level Soft-Voting Ensemble (CLSVE) is used for intrusion detection. Through literature research and extensive empirical analysis, the Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) are selected as the base learners [14; 15; 16; 17]. On the other hand, the Chaos Bat Algorithm with Opposition-Based Learning (OBL) is used for the weight optimization of the ensemble. The Bat Algorithm (BA) has better performance than the Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO) [18]. However, it still has the disadvantages of the declining population diversity and local convergence in the searching processes [19; 20]. Note that the combination of chaos and metaheuristic algorithms is a promising research field [21]. Due to its dynamic characteristics, chaotic mapping has been widely recognized in the field of optimization, which makes the Optimization Algorithm explore the search space more actively and globally. Moreover, the OBL strategy [22], which aims to boost the efficiency of the Bat Algorithm, is also used for the initialization of the population. Therefore, a new soft-voting scheme based on the Chaos Bat Algorithm with Opposition-Based Learning is used to combine the base learners of the ensemble. In short, the main contributions of this paper are as follows:  
1. An improved ensemble framework named CBA-CLSVE is proposed. The Class-Level Soft-Voting Ensemble (CLSVE) is selected for intrusion detection. 2. The Chaos Bat Algorithm (CBA) with the Opposition-Based Learning method is used to generate class-level weights to create the weighted voting ensemble. 3. The soft-voting schemes are compared with the hard-voting methods based on the same learners. We also compare the performance of different voting ensemble methods with or without feature selection. It is verified that the Class-Level Soft-Voting method based on the CBA can be used to improve the ensemble performance. It also shows that the same performance of the ensemble can be obtained with half the total number of features or fewer. The organization of the paper is as follows. The related work is provided in Section 2. Section 3 presents the background including the hard-voting and soft-voting method, the base learners and the basic Bat Algorithm. The proposed CBA-CLSVE framework for intrusion detection is described in Section 4. The experimental results are introduced in Section 5. The paper is concluded in Section 6.