Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework
Joint Authors
Chen, Hui-ling
Li, Yuping
Tu, Jixia
Lin, Aiju
Li, Chengye
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-20
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Tu, Jixia& Lin, Aiju& Chen, Hui-ling& Li, Yuping& Li, Chengye. 2019. Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1194596
Modern Language Association (MLA)
Tu, Jixia…[et al.]. Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework. Mathematical Problems in Engineering No. 2019 (2019), pp.1-16.
https://search.emarefa.net/detail/BIM-1194596
American Medical Association (AMA)
Tu, Jixia& Lin, Aiju& Chen, Hui-ling& Li, Yuping& Li, Chengye. Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1194596
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194596