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

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

HUANG Hui1,2, FENG Xi-an1, WEI Yan3, XU Chi3, CHEN Hui-ling2   

  1. 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

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

CLC Number: 

  • TP393
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