Application of Classification Model in Predicting Academic Talent Capacity in Talent Introduction
Talent introduction is an important force of academic development in universities.As the core of talent introduction, prediction of academic talent capacity is an essential and valuable research.Data mining approaches are good at analyzing information, extracting patterns or rules from a big dataset and then making a prediction based on the relationship among extracted information.In this study, whether a talent could obtain a Natural Science Foundation of China (NFC) in three years after s/he is recruited to the university is regarded as an evaluation indicator of academic talent capacity.A classical classification model, Gradient Boost Decision Tree, is used as the primary analytic model to predict this evaluation indicator.In order to validate the effectiveness of the model, other five classification models are used to conduct a comparative experiment based on prediction accuracy values and the F-measure metric.Further, to investigate the contribution of some important features, we make a marginal utility analysis of important features which have a high correlation with academic talent capacity.
data mining classification models prediction talent introduction academic talent capacity
Shunshun Shi Mingzhou Chen Rui Feng Hua Zhang Shuai Zhang
School of Information Zhejiang University of Finance and Economics Hangzhou,China
国际会议
郑州
英文
135-140
2018-09-21（万方平台首次上网日期，不代表论文的发表时间）