Uncertainty-Driven Entrepreneurial Career Education Decision-Making in College Incubators: A Neutrosophic Vague N-Soft Set Approach - Bingquan Yin; Yali Hou; Rui Zhao; Xiangge Liu (Neutrosophic Sets and Systems)
Entrepreneurship education has been demonstrating an important role in shaping the career trajectories of university students. Nevertheless, deciding the entrepreneurial potential leftovers as a complicated procedure because of inherent uncertainty in student characteristics. In this study, we propose a Neutrosophic Vague N-Soft Set-based Gradient Boosting (NVNSS-GB) approach to offer an uncertaintydriven decision about the likeliness of students to become entrepreneurs. Our approach introduces novel application NVNSS theory to transform student attributes into truth, falsity, and indeterminacy membership intervals to provide effective handling of vagueness and hesitancy in their responses. Then, we introduce an N-Soft Set classification method to assign a multi-level graded evaluation to university students. Next, we fed the generated enriched feature representation to GB to decide on if a student is likely to be an entrepreneur. Comprehensive analyses on a real case study is conducted using sensitivity analysis, feature importance ranking, ROC-AUC evaluation, and complexity analysis, demonstrated the efficiency, effectiveness, and interpretability of NVNSS-GB. Our findings implied that NVNSS-GB has promising power in improving decision-making within entrepreneurship education, offering an insightful tool for career guidance that can allow universities to design targeted interventions to foster entrepreneurial skills among students.