基于增强核极限学习机的专业选择智能系统
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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。
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.
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.
引文
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[13]Duan L,Dong S,Cui S,et al.Extreme learning machine with gaussian kernel based relevance feedback scheme for image retrieval[J].Springer International Publishing,2016,1:397-408.
[14]Chang C C,Lin C J.LIBSVM:a library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology,2011,2(3):1-27.
[2]Guo W W.Incorporating statistical and neural network approaches for student course satisfaction analysis and prediction[J].Expert Systems with Applications,2010,37(4):3358-3365.
[3]Campagni R,Merlini D,Sprugnoli R,et al.Data mining models for student careers[J].Expert Systems with Applications,2015,42(13):5508-5521.
[4]Huang C,Xu J.Second major selection system based on data mining[J].Journal of Information and Computational Science,2009,6(6):2333-2340.
[5]Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1-3):489-501.
[6]Huang G B,Zhou H,Ding X,et al.Extreme learning machine for regression and multiclass classification[J].IEEE Transactions on Systems Man&Cybernetics Part B Cybernetics,2012,42(2):513-529.
[7]Liu B,Tang L W,Wang J B,et al.2-D defect profile reconstruction from ultrasonic guided wave signals based on QGA-kernelized ELM[J].Neurocomputing,2014,128:217-223.
[8]Chen H L,Wang G,Ma C,et al.An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease[J].Neurocomputing,2016,184(C):131-144.
[9]Liu T,Hu L,Ma C,et al.A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection[J].International Journal of Systems Science,2015,46(5):919-931.
[10]Chen H L,Yang B,Wang S J,et al.Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy[J].Applied Mathematics and Computation,2014,239:180-197.
[11]代琨,于宏毅,仇文博,等.基于SVM的网络数据无监督特征选择算法[J].吉林大学学报:工学版,2015,45(2):576-582.Dai Kun,Yu Hong-yi,Qiu Wen-bo,et al.Unsupervised feature selection algorithm based on support vector machine for network data[J].Journal of Jilin University(Engineering and Technology Edition),2015,45(2):576-582.
[12]赵东,臧雪柏,赵宏伟.基于果蝇优化的随机森林预测方法[J].吉林大学学报:工学版,2017,47(2):609-614.Zhao Dong,Zang Xue-bai,Zhao Hong-wei.Random forest prediction method based on Optimization of fruit fly[J].Journal of Jilin University(Engineering and Technology Edition),2017,47(2):609-614.
[13]Duan L,Dong S,Cui S,et al.Extreme learning machine with gaussian kernel based relevance feedback scheme for image retrieval[J].Springer International Publishing,2016,1:397-408.
[14]Chang C C,Lin C J.LIBSVM:a library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology,2011,2(3):1-27.