基于增强核极限学习机的专业选择智能系统

doi:10.13229/j.cnki.jdxbgxb20170307

吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (4): 1224-1230.doi: 10.13229/j.cnki.jdxbgxb20170307

基于增强核极限学习机的专业选择智能系统

黄辉^1,2, 冯西安¹, 魏燕³, 许驰³, 陈慧灵²

1.西北工业大学航海学院,西安 710072;
2.温州大学数理与电子信息工程学院,浙江温州 325035;
3.温州科技职业学院信息技术学院,浙江温州 325006

收稿日期:2017-03-18 出版日期:2018-07-01 发布日期:2018-07-01
通讯作者: 魏燕(1983-),女, 高级工程师.研究方向:信息系统,数据挖掘.E-mail:weiyan@wzvcst.edu.cn
作者简介:黄辉(1982-),男, 实验师, 博士研究生.研究方向:图像处理,机器学习.E-mail:huanghui@wzu.edu.cn
基金资助:
国家自然科学基金项目(61101155); 浙江省自然科学基金项目(LY15F020033); 温州市科技计划项目(2016R0002); 浙江省教育厅科学研究基金项目(Y201533884); 浙江省科技计划项目(2014C32031).

An intelligent system based on enhanced kernel extreme learning machine for choosing the second major

HUANG Hui^1,2, FENG Xi-an¹, WEI Yan³, XU Chi³, CHEN Hui-ling²

1.School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China;
2.College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China;
3.Wenzhou Vocational College of Science and Technology, Wenzhou 325006, China

Received:2017-03-18 Online:2018-07-01 Published:2018-07-01

摘要/Abstract

摘要： 基于粒子群优化(PSO)的增强型核极限学习机(KELM)提出了一种有效的预测模型PSO-KELM来辅助第二专业选择。在PSO-KELM 中,PSO策略确定KELM的最佳参数。PSO-KELM与其他两个竞争方法在学生专业选择数据上通过10折交叉验证方案进行比较,这两个方法分别是支持向量机和网格搜索技术优化的KELM。结果表明了本文预测模型在分类精度、受试者工作特征曲线面积(AUC)、灵敏度和特异性方面的优越性。

关键词: 计算机应用, 核极限学习机, 粒子群优化, 第二专业选择

Abstract: This paper proposes an effective prediction model for choosing the second major based on the Particle Swarm Optimization (PSO) enhanced Kernel Extreme Learning Machine (KELM), which is called PSO-KELM model. In this model, the PSO strategy is adopted to adaptively determine the optimal parameters in KELM. The PSO-KELM model is compared with other two competitive methods, including Support Vector Machine (SVM) and a KELM is optimized by grid search technique, on a major selection dataset via a 10-fold cross validation scheme. The results clearly confirm the superiority of the proposed PSO-KELM model in classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity and specificity.

Key words: computer application, kernel extreme learning machine, particle swarm optimization, second major selection

中图分类号:

TP393

黄辉, 冯西安, 魏燕, 许驰, 陈慧灵. 基于增强核极限学习机的专业选择智能系统[J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.

HUANG Hui, FENG Xi-an, WEI Yan, XU Chi, CHEN Hui-ling. An intelligent system based on enhanced kernel extreme learning machine for choosing the second major[J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.

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基于增强核极限学习机的专业选择智能系统

An intelligent system based on enhanced kernel extreme learning machine for choosing the second major

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