基于反向细菌优化支持向量机的躯体化障碍预测模型

doi:10.13229/j.cnki.jdxbgxb20170346

吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 936-942.doi: 10.13229/j.cnki.jdxbgxb20170346

基于反向细菌优化支持向量机的躯体化障碍预测模型

蔡振闹¹, 吕信恩², 陈慧灵³

1.西北工业大学计算机学院,西安710072;
2.温州大学心理健康教育中心,浙江温州325035;
3.温州大学数理与电子信息工程学院,浙江温州325035;

收稿日期:2017-04-12 出版日期:2018-05-20 发布日期:2018-05-20
通讯作者: 吕信恩(1983-), 男, 讲师, 硕士.研究方向:心理健康教育,心理测量.E-mail:luxinen@163.com
作者简介:蔡振闹(1981-),男, 工程师, 博士研究生.研究方向:机器学习,数据挖掘.E-mail:erikcai@mail.nwpu.edu.cn
基金资助:
浙江省自然科学基金项目(LY17F020012,Y14F020126); 温州市科技计划项目(ZG2017019).

Prediction model of somatization disorder based on an oppositional bacterial foraging optimization based support vector machine

CAI Zhen-nao¹, LYU Xin-en², CHEN Hui-ling³

1.School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
2.Mental Health Education Center, Wenzhou University, Wenzhou 325035, China;
3.College of Mathematics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China

Received:2017-04-12 Online:2018-05-20 Published:2018-05-20

摘要/Abstract

摘要： 基于改进的细菌优化算法(BFO)的支持向量机(SVM)提出了一种有效的智能预警模型(IBFO-SVM),并对社区矫正人员出现的躯体化障碍严重程度进行了预测。在IBFO-SVM 中,在细菌优化算法中引入反向学习策略,增加细菌种群的多样性,并使初始种群的个体尽可能均匀分布,提高BFO优化过程的收敛速度同时减少了BFO陷入局部最优解的概率。然后利用提出的反向细菌优化算法(IBFO)来确定SVM的两个最优参数(惩罚系数和核宽)。最后,将IBFO-SVM模型与基于原始细菌优化的SVM模型(BFO-SVM)、基于遗传算法的SVM模型(GA-SVM)以及基于粒子群优化算法的SVM模型(PSO-SVM)在心理评测数据上通过10折交叉验证方法进行了比较。实验结果表明:提出的IBFO-SVM预测模型在分类准确率、马修斯相关系数(MCC)、灵敏度和特异性方面比其他方法具有更好的性能,可以很好地将重度躯体化障碍和轻度躯体化障碍进行诊断。

关键词: 计算机应用, 躯体化障碍, 支持向量机, 反向学习, 细菌优化算法

Abstract: The proposed Bacterial foraging optimization algorithm (BFO) based on support vector machine (SVM) presents an effective intelligent early-warning model (IBFO-SVM) for the somatization disorder severity prediction for Community Correction patients. In IBFO-SVM, opposition-based learning strategy is introduced into the BFO to increase the diversity of the bacterial population and uniformly distributed as far as possible for initial population, which improves the convergence rate of BFO and reduces the probability which falls into the local optimal solution. The proposed improved bacterial foraging optimization algorithm (IBFO) is used to determine the two optimal parameters of SVM (penalty coefficient and kernel width). For comparison purpose, the original BFO, genetic algorithm (GA), particle swarm optimization (PSO) has also been proposed to optimize the parameters of SVM. In this study, IBFO-SVM, BFO-SVM, GA-SVM and PSO-SVM model were compared by 10-fold cross-validation method in psychological evaluation data. The experimental results demonstrate that the proposed IBFO-SVM method has better performance in predicting the severe somatization disorder and mild somatization disorder in terms of classification accuracy, Mathews correlation coefficient (MCC), sensitivity and specificity than other methods.

Key words: computer application, somatization disorder, support vector machine, opposition-based learning, bacterial foraging algorithm

中图分类号:

TP393

蔡振闹, 吕信恩, 陈慧灵. 基于反向细菌优化支持向量机的躯体化障碍预测模型[J]. 吉林大学学报(工学版), 2018, 48(3): 936-942.

CAI Zhen-nao, LYU Xin-en, CHEN Hui-ling. Prediction model of somatization disorder based on an oppositional bacterial foraging optimization based support vector machine[J]. 吉林大学学报(工学版), 2018, 48(3): 936-942.

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基于反向细菌优化支持向量机的躯体化障碍预测模型

Prediction model of somatization disorder based on an oppositional bacterial foraging optimization based support vector machine

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