Computer Science ›› 2015, Vol. 42 ›› Issue (Z11): 1-4.

    Next Articles

Customer Classification Model of Employers by Using BP Neural Networks

QIAO Fei and GE Yan-hao   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Customer classification of employers brings great benefits for correctly evaluating the category of each employers,which helps decision-makers to appraise the corporation efficiency when they corporate with each customer.In order to give quantitative model to help solving this problem,we extracted raw data series from the educational management information system and built a classification calculating model based on BP neural network.Then we managed to get each value of the parameters in the model by training the historical datasets and compared the result of prediction with other methods which are widely used nowadays to solve this problem.Finally we made a conclusion that customer classification of employers for career guiding service by using BP neural network model performs better than other existing solutions and gives more efficient supports to management layer of universities and government by making comparably precise predictions.

Key words: BP neural network,Data mining,Data classification problem,Career guiding information service

[1] Baker R S.Educational Data Mining:An Advance for Intelligent Systems in Education[J].IEEE Intelligent Systems,2014,29(3):1541-1672
[2] Pea-Ayala A.Educational data mining:A survey and a datamining-based analysis of recent works[J].Expert Systems with Applications,2014,41(4):1432-1462
[3] Sen B,Uar E.Predicting and analyzing secondary educationplacement-test scores:A data mining approach[J].Expert Systems with Applications,2012,39(10):9468-9476
[4] Borkar S,Rajeswari K.Predicting Students Academic Performance Using Educational Data Mining[J].International Journal of Computer Science and Mobile Computing,2013,2(7):273-279
[5] Qin Yu-Quan,Li Hai-Min.Sales Forecast Based on BP Neural Network[C]∥IEEE 3rd International Conference on Communication Software and Networks.2011:186-189
[6] Wang Rui,Wang Xu.Pulse frequency classification based on BP neural network[J].Journal of Harbin Engineering University,2006,27(suppl.):471-473
[7] Tian Guo-yu,Huang Hai-yang.Identify of hidden layer in Neural network[C]∥Information Technology,2010(10):79-81
[8] Xu Xin,Xu Li-hong.Coal requirement prediction using BP neural network[C]∥International Conference on E-Business and E-Government(ICEE).2010
[9] Hsiao W-H,Chang T-S.Selection criteria of recruitment for information systems employees:Using the analytic hierarchy process (AHP) method[J].African Journal of Business Mana-gement 2011,5(15):6201-6209
[10] Bakaev M,Avdeenko T.Data Extraction for Decision-Support Systems:Application in Labour Market Monitoring and Analysis[J].International Journal of e-Education,e-Business,e-Mana-gement and e-Learning,2014,4(1):23-27
[11] 陈笑怡.泛在学习中教学质量评价的数据挖掘研究[D].上海:上海交通大学现代远程教育研究中心,2011
[12] Shang Xiao,Chisholm L A.Classification of Australian Native Forest Species Using Hyperspectral Remote Sensing and Machine-Learning Classification Algorithms[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2013,7(6):2481-2489
[13] Luan Jing.Data Mining and Its Applications in Higher Education[M]∥New Directions for Institutional Research.Spring,2002
[14] Wanga Feng-hsu,Shaob Hsiu-mei.Effective personalized recommendation based on time-framed navigation clustering and association mining[J].Expert Systems with Applications,2004,27(3):365-377
[15] Sembiring S,Zarlis M.Prediction of student academic perfor-mance by an application of data mining techniques[C]∥International Conference on Management and Artificial Intelligence(IPEDR).2011
[16] 翼正强.基于Web数据分析的就业信息服务平台的设计实现[D].山东:山东大学软件学院,2013
[17] 吕守涛.数据挖掘技术在毕业生就业工作中的应用研究[D].成都:电子科技大学,2007

No related articles found!
Full text



[1] . [J]. Computer Science, 2018, 1(1): 1 .
[2] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[3] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[4] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[5] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[6] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[7] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[8] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[9] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[10] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .