## 3 Proposed IBFO-KELM Model  
In this study, a novel evolutionary KELM model was developed using the IBFO strategy, as shown in Figure 1. The two key parameters of the proposed KELM model were automatically tuned by using the IBFO strategy to simulate the foraging behaviors of the BFO model and its interactions with the surrounding environment. The proposed model was comprised of two main procedures, including inner parameter optimization and outer performance evaluation. During the inner parameter optimization procedure, the penalty parameters \(C\) and kernel bandwidth \(\gamma\) of the KELM were determined dynamically using the IBFO technique via a five-fold cross validation (CV) analysis. Then, the obtained optimal parameter pair (\(C\), \(\gamma\)) was inputted into the KELM prediction model in order to perform the classification task in the outer loop using a 10-fold CV strategy. The classification error rate was used as the fitness function. \[fitness=\left(\sum_{i=1}^{K}testError_{i}\right)/k \tag{10}\]  
where \(testError_{i}\) represents the average test error rates achieved by the KELM classifier via the five-fold CV during the inner parameter optimization procedure. Figure 1: Flowchart of the improved bacterial foraging optimization strategy. The pseudo-code of the IBFO strategy is described in detail as follows in Algorithm 1:  
```  Initialize dimension \(p\), population \(S\), chemotactic steps \(Nc\), swimming length \(Ns\), reproduction steps \(Nre\), elimination-dispersal steps \(Ned\), elimination-dispersal probability \(Ped\), step size \(C(i)\). Calculate the corresponding opposite solutions of bacterial populations based on opposition-based learning. From the original and its corresponding opposite solutions of bacterial populations, \(S\) superior individuals are selected as the initial solutions of bacterial populations. for\(ell=1\):\(Ned\) for\(K=1\):\(Nre\) for\(j=1\):\(Nc\)  Intertime = Intertime + \(1\); for\(i=1\):\(s\)  J(i,j,\(K\),\(ell\)) = \(fobj\)(\(P\)(:,\(i\),\(j\),\(K\),\(ell\)));  Jlast = \(J\)((i,j\),\(K\),\(ell\));  Tumble according to Equation (5) \(m=0\); while\(m<Ns\) \(m=m+1\); if\(|(i,j+1,\)\(K\),\(ell)<|last\)  Jlast = \(J\)((i,j+1,\)\(K\),\(ell\));  Tumble according to Equation (5) else \(m=Ns\);  End End End  End /*Reproduction*/  Jhealth = sum([(:,:,\(K\),\(ell\)),2); [(\(|health,sortind\)) = sort(\(|health\)); \(P\)(:,:,\(1\),\(K\) + \(1\),\(ell\)) = \(P\)(:,\(sortind\),\(Nc+1,\)\(K\),\(ell\)); for\(i=1\):\(Sr\) \(P\)(:,\(i+Sr,\)\(1\),\(K+1,\)\(ell\)) = \(P\)(:,\(i\),\(1\),\(K+1,\)\(ell\));  End End /*Elimination-Dispersal*/ for\(m=1\):\(s\) if\(Ped>rand\)  Reinitialize bacteria \(m\)  End



## 4 Experimental Design

### Sonatization Disorder Data Description

The data obtained from this research are all from the jurisdiction of Wenzhou Municipal Bureau of Justice. A total of 419 community correction objects were selected as the research objects. These research objects are mainly inmates with mild offences, little subjective viciousness, including inmates who were sentenced to public surveillance, have been suspended, with temporary sentence execution outside a prison and a ruling by parole personnel, etc. In this study, the symptom self-rating scale (SCL-90) was used to study psychological and physical symptoms, depression, hostility and psychosis in the recent week. Among them, somatization symptoms mainly reflect subjective bodily discomfort, with 12 attributes in all. The range of values for features F1-F12 is {1, 2, 3, 4, 5}, representing five different intensities of a given symptom. The range of values for the decision attribute F13 is {1, 2}, representing a severe state and the mild state, respectively. Table 1 gives a description of the 13 attributes. ### Experimental Setup

The computational analysis was conducted on a Windows 7 operating system with an AMD Athlon 64 X2 Dual Core Processor 5000+ (2.6 GHz) (AMD Company, Santa Clara, CA, USA) and 4 GB of RAM.