Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer

Yan Wei,Huiling Chen,Mengxiang Chen,Mingjing Wang,Huijing Lv,Ali Asghar Heidari,Chengye Li

doi:10.1109/access.2020.2982796

Abstract

This study aims to propose an effective intelligent model for predicting entrepreneurial intention, which can provide a reasonable reference for the formulation of talent training programs and the guidance of entrepreneurial intention of students. The prediction model is mainly based on the kernel extreme learning machine (KELM) optimized by the improved Harris hawk’s optimizer (HHO). In order to obtain better parameters and feature subsets, the Gaussian barebone (GB) strategy is introduced to improve the HHO algorithm, so as to strengthen the optimization ability for tuning parameters of KELM and identifying the compact feature subsets. Then, an optimal KELM model (GBHHO-KELM) is established according to the obtained optimal parameters and feature subsets to predict the entrepreneurial intention of students. In the experiment, GBHHO is compared with the other nine well-known methods in 30 CEC 2014 benchmark problems. The experimental findings suggest that the proposed GBHHO method is significantly superior to the existing methods in most problems. At the same time, GBHHO-KELM is compared with other machine learning methods in the prediction of entrepreneurial intention. The experimental results indicate that the proposed GBHHO-KELM can achieve better classification performance and higher stability in accordance with the four metrics. Therefore, we can conclude that the GBHHO-KELM model is expected to be an effective tool for the prediction of entrepreneurial intention.

Highlights

Accompanied the accelerating development and constant large-scale enrollment expansion of higher education in China, with the growth of the graduate’s number, the college students’ pressure on employment is raising
After that, compared with the previous traditional training techniques, the extreme learning machine (ELM) method can significantly improve both learning speed and performance
PREDICTION RESULTS OF ENTREPRENEURIAL INTENTION In this experiment, we evaluated the effectiveness of the GBHHO-kernel extreme learning machine (KELM) with the FS model versus other peers

Summary

Introduction

Accompanied the accelerating development and constant large-scale enrollment expansion of higher education in China, with the growth of the graduate’s number, the college students’ pressure on employment is raising. A Gaussian barebone (GB) enhanced Harris hawk’s optimizer (HHO) is proposed to train an optimal kernel extreme learning machine (KELM) model, and the resultant GBHHO-KELM is proven for the first time to predict the students’ entrepreneurial intention. The improved HHO was employed to train a robust and optimal KELM model to predict the pre-graduate students’ entrepreneurial propensity.

Objectives

Results

Conclusion