## 1 Introduction  
Since the middle of the 20 th century, all countries in the world have been reforming the penal system to explore more humane, scientific and effective punishment methods to improve the psychology and behavior of criminals. Community correction systems are an entirely new way of punishment under such innovation background. According to statistics, there are about 700,000 community correction objects in our country. In order to understand the psychological and behavioral characteristics of community correction objects, a self-rating scale (SCL-90) [1,2] is generally adopted to evaluate their mental health level. On the scale, somatization disorder can be reflected by 12 factors. Somalization factors mainly reflects subjective physical discomfort, including the main discomfort in cardiovascular, gastrointestinal, respiratory system as well as headaches, back pain, muscle pain and other physical performance of anxiety. According to the research, the application of artificial intelligence technology in this area has not yet been reported. This paper proposed a new machine learning framework to predict the severity of somatization disorder for the first time. In order to improve the accuracy rate of assessment of the severity of the somatization disorder, this paper proposes a kernel extreme learning machine based on opposition-based BFO method (improved bacterial-foraging, optimization-based kernel extreme learning machine, IBFO-KELM). As a major machine learning model, a kernel extreme learning machine (KELM) develops on the basis of an extreme learning machine with the integration of the kernel concept [3]. Due to its unique advantages, KELM has been widely used in some classification tasks [4; 5; 6; 7; 8; 9; 10; 11] and performed well especially in computer-assisted medical diagnosis [7; 8; 9; 10; 11]. However, KELM's performance is mainly influenced by the parameters in its models, and the research shows that the accuracy of its classification can be greatly improved by establishing appropriate model parameter settings. Therefore, the key parameters should be set to the appropriate values before they are applied to the actual problem, such as penalty factor and width of kernel function. Traditionally, these parameters are treated by means of grid search and gradient descent. However, these methods are easy to get into local optimal solutions. Recently, some bio-inspired meta heuristic search algorithms (such as genetic algorithms (GA) [15; 20; 21], particle swarm optimization algorithms (PSO) [18; 22; 23], the grey wolf optimization algorithms (GWO) [13]) have made it easier to find the global optimal solution, compared with the traditional method. Based on Escherichia coli's food consuming behavior in the human intestine, the bacteria optimization algorithm (BFO) was proposed by [PERSON] in 2002 as a new kind of bionic algorithm [24] that imitates four kinds of intelligent behavior, such as the bacterial foraging trend, as well as cluster, copy, and disperse. This algorithm is another hot spot in the field of bio-heuristic computing due to its advantages, such as the parallel search of the swarm intelligence algorithm and that it can easily jump out of local minima. The BFO algorithm has been widely used in many fields [25; 26; 27; 28; 29; 30; 31; 32; 33] since it was put forward. However, in the process of optimization, BFO is extremely easy to get into the local optimum [34; 35; 36], and it is difficult to find the global optimal solution. Aiming at resolving this problem, this paper introduces opposition-based learning strategy [37] into BFO as an improved bacterial foraging optimization algorithm (IBFO) to improve the population diversity and the rate of convergence. The principle of opposition-based learning strategy [37] is to generate corresponding opposite solutions for each initial candidate solution. From these two kinds of solutions (candidate solutions and corresponding opposite solutions), the solutions with relatively better fitness are selected as members of the initial populations.