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Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

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  • Jixia Tu
  • Aiju Lin
  • Huiling Chen
  • Yuping Li
  • Chengye Li

Abstract

Under the background of “innovation and entrepreneurship,” how to scientifically and rationally choose employment or independent entrepreneurship according to their own comprehensive situation is of great significance to the planning and development of their own career and the social adaptation of university personnel training. This study aims to develop an adaptive support vector machine framework, called RF-CSCA-SVM, for predicting college students' entrepreneurial intention in advance; that is, students choose to start a business or find a job after graduation. RF-CSCA-SVM combines random forest (RF), support vector machine (SVM), sine cosine algorithm (SCA), and chaotic local search. In this framework, RF is used to select the most important factors; SVM is employed to establish the relationship model between the factors and the students’ decision to choose to start their own business or look for jobs. SCA is used to tune the optimal parameters for SVM. Additionally, chaotic local search is utilized to enhance the search capability of SCA. A total of 300 students were collected to develop the predictive model. To validate the developed method, other four meta-heuristic based SVM methods were used for comparison in terms of classification accuracy, Matthews Correlation Coefficients (MCC), sensitivity, and specificity. The experimental results demonstrate that the proposed method can be regarded as a promising success with the excellent predictive performance. Promisingly, the established adaptive SVM framework might serve as a new candidate of powerful tools for entrepreneurial intention prediction.

Suggested Citation

  • Jixia Tu & Aiju Lin & Huiling Chen & Yuping Li & Chengye Li, 2019. "Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-16, January.
  • Handle: RePEc:hin:jnlmpe:2039872
    DOI: 10.1155/2019/2039872
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    Cited by:

    1. Chen, Chengcheng & Wang, Xianchang & Yu, Helong & Wang, Mingjing & Chen, Huiling, 2021. "Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 291-318.
    2. Lin, Chao & Cai, Peipei, 2023. "Analyzing the impacts of natural resource utilization and green economic growth in China: Evidence from a econometric analysis," Resources Policy, Elsevier, vol. 81(C).
    3. Wang, Lin & Dilanchiev, Azer & Haseeb, Mohammad, 2022. "The environmental regulation and policy assessment effect on the road to green recovery transformation," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 914-929.
    4. Fan, Yi & Wang, Pengjun & Heidari, Ali Asghar & Chen, Huiling & HamzaTurabieh, & Mafarja, Majdi, 2022. "Random reselection particle swarm optimization for optimal design of solar photovoltaic modules," Energy, Elsevier, vol. 239(PA).
    5. Bai, Bing, 2023. "Fiscal stimulus and natural resource efficiency: A comprehensive approach to a green economic recovery," Resources Policy, Elsevier, vol. 86(PB).

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