CAJ | 학술논문

以相关文献作为研究基础，确定知识型员工离职的相关因素，利用粗糙集理论对知识的约简能力及神经网络的分类能力，构建粗糙集-神经网络（RS—ANN）员工离职预警模型；并将该模型应用于员工离职预警数据进行实例验证，该模型预警速度快，离职预警正确率高。
이상관문헌작위연구기출，학정지식형원공리직적상관인소，이용조조집이론대지식적약간능력급신경망락적분류능력，구건조조집-신경망락（RS—ANN）원공리직예경모형；병장해모형응용우원공리직예경수거진행실례험증，해모형예경속도쾌，리직예경정학솔고。
As the basic study to related documents, we found the relevant factors of knowledge-based employee turnover. Considering the reduction ability of rough set theory and the classification ability of artificial neural network, a rough set-neural network (RS-ANN) employee turnover early-warning model is constructed. Then the model is applied to employee turnover early-warning data for instance validation. The results verify that the model has fast warning speed and high accuracy.