Earticle

초록

영어

In this paper, a novel prediction method for box-office is proposed based on the rough set data processing function and support vector machine (SVM) classification mechanism. The front-end processor, optimizes the input variables by attribute reduction, in order to improve the performance of classifier. Then, in view of the lack of guidance of scientific theory problem of domestic movie box office prediction, the classifier for the box-office prediction, the influence factors of the box office revenue as the input variables, the box-office income categories as output variables, data preprocessing and training test. Results show that the classifier can effectively solve the box office prediction problem, the results of the multilayer perceptron is better than that of Ramesh S. and Dursun D. using the prediction method, and the prediction error is less than 10%, to meet the requirements of the film market, show the powerful classification ability.

목차

Abstract
 1. Introduction
 2. Basics of the Rough Set Theory
  2.1. Classic Rough Set Theory
  2.2. Fuzzy Rough Set
 3. Basics Principle of SVM
 4. Prediction Model
 5. Prediction Results Analysis
  5.1 Cross Validation Strategy
  5.2 Performance Metrics of Ticket Sales Prediction
  5.3 Prediction Results and Performance Analysis
 5. Conclusion
 References

키워드

저자

• Ling Liu [ School of Information Technology and Engineering Tianjin University of Technology and Education, Tianjin, 300222, China ]
• Yang Zhao [ Department of Electronic and Information Technology, Jiangmen Polytechnic, Jiangmen, 529090, China, College of Instrumentation Science and Electrical Engineering, Jilin University, Changchun, 130000, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605