摘要
毕业生就业率是评价一个高校学生质量的一个重要指标,毕业生就业率建模与预测对高校就业工作具有重要的指导意义。由于毕业生就业率的影响因素多,使得毕业生就业率具有比较强的随机性和混沌变化特点,为了提高毕业生就业率预测精度,提出了混沌分析和最小二乘支持向量机的毕业生就业率预测模型。根据Takers定理,结合毕业生就业率变化特点,引入混沌分析理论对毕业生就业率历史数据进行相空间重构,然后利用相空间重构后的毕业生就业率的历史数据训练最小二乘支持向量机,根据最优参数建立毕业生就业率预测模型,最后与当前经典毕业生就业率预测模型进行了仿真对比分析,结果表明这个毕业生就业率模型预测精度平均值超过了93%,预测误差要明显少于当前经典模型,同时简化了毕业生就业率预测建模过程,减少了预测时间,获得了更优的毕业生就业率预测结果。
The employment rate of graduates is an important index to evaluate the quality of college students.Modeling and predicting graduates’employment rate is of great guiding significance to the employment work of colleges and universities.There are many factors influencing the graduate employment rate,which makes the graduate employment rate have strong randomness and chaos.In order to improve the prediction accuracy of graduate employment rate,a prediction model of graduate employment rate based on chaos analysis and least squares support vector machine is designed.According to the Takers theorem,combined with the change characteristics of graduate employment rate,the chaos theory is introduced to reconstruct the phase space of the historical data of graduate employment rate,and then the least square support vector machine is trained with the historical data of the graduate employment rate after phase space reconstruction.According to the optimal parameters,a prediction model of graduate employment rate is established.Finally,it is compared with the current classic employment rate prediction model the results show that the average prediction accuracy of the model is more than 93%,and the prediction error is obviously less than the current classic model.At the same time,it simplifies the modeling process of graduate employment rate prediction,reduces the prediction time of graduate employment rate,and obtains better prediction results of graduate employment rate.
作者
翟晓鹤
ZHAI Xiaohe(School of Nursing,Xinjiang Medical University,Urumqi 830054,China)
出处
《微型电脑应用》
2021年第2期169-172,共4页
Microcomputer Applications
