Earticle

초록

영어

In the field of application prediction, there are two kinds of common methods to establish the prediction model: prediction model established by artificial data analysis relying on expert experience, and prediction model achieved by statistical model exploiting data analysis. However, the prediction accuracy of the model based on expert is restricted by the experiences, while the model based on statistical analysis is limited by the quality and scale of the training data. In view of the advantages and disadvantages of these two kinds of models, this paper presents a prediction model by integrating Analytic Hierarchy Process and Logistic Regression. The proposed prediction model uses the Analytic Hierarchy Process, which are based on the training data and expert experience to obtain the rank of predominant factor in a specific domain, and exploits the logistical regression model to learn the weights of each influencing factor. Finally, the linear combination of the two models is used to obtain the prediction model. Further, we take agricultural land contractual management transfer prediction as an example to test the proposed hybrid prediction model.

목차

Abstract
 1. Introduction
 2. Prediction Model based on Analytic Hierarchy Process and Logistic Regression
  2.1. Prediction Model
  2.2. Analytic Hierarchy Process
  2.3. Prediction based on Logistical Regression
 3. Features for Agricultural Land Contractual Management Transfer Prediction
 4. Experiment and Result Analysis
  4.1. Experimental Data
  4.2. Experiment Settings
  4.3. Experimental Results and Analysis
 5. Conclusion
 References

키워드

저자

• Hai-ying Cao [ Northeast Agricultural University, Harbin, China / Harbin Finance University, Harbin, China ]
• Ling Wu [ Northeast Agricultural University, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605