MAKİNE ÖĞRENMESİ ALGORİTMALARI KULLANARAK GİŞE HASILATININ TAHMİNİ

Abstract

Yeni efektler ve 3 boyutlu çekimler gibi güncel gelişmeler film endüstrisindeki rekabeti arttırmaktadır. Film endüstrisindeki pahalı ve riskli yatırımlar için üretim öncesi analizler giderek önem kazanmaktadır. Bu noktada, gişe hasılatı tahmini önemli bir araştırma konusu olmuştur. Bu bağlamda, bu çalışma gişe hasılatı tahmini için makine öğrenmesi algoritmaları kullanarak bir yaklaşım sunmayı amaçlamaktadır. Geleneksel yapay zeka metotlarından yapay sinir ağları ve destek vektör makineleri algoritmaları, karar ağaçları algoritmalarından rastgele ağaç, rastgele orman ve C4.5 algoritmaları kullanılmıştır. Daha sonra, bu algoritmalar ile topluluk algoritmalarından torbalama algoritması kullanılarak melez bir model önerilmiştir. Tahmin modelleri doğru sınıflandırma yüzdesi, kappa istatistiği, ROC alanı ile değerlendirilmiştir. Sayısal sonuçlar, rastgele orman-torbalama ve yapay sinir ağları-torbalama melez metotlarının tüm modeller arasında en iyi performansa sahip olduğunu göstermektedir. 

Keywords

• : Gişe hasılatı,Yapay sinir ağları,Destek vektör makineleri,Karar ağaçları,Makine öğrenmesi

FORECASTING OF BOX OFFICE REVENUE USING MACHINE LEARNING ALGORITHMS

Abstract

Current developments such as new effects and 3D shootings increase the competition in the movie industry. Pre-production analyzes are becoming more important for the expensive and risky investments in the movie industry. At this point, the prediction of the box office revenue has become an important research issue. In this context, this study aims to present an approach using machine learning algorithms for box-office revenue prediction. Artificial neural networks and support vector machines algorithms as traditional artificial intelligence methods and random trees, random forests and C4.5 algorithms as decision tree algorithms are used. Later, a hybrid model is proposed using these algorithms and the bagging algorithm from the ensemble algorithm. Prediction models are evaluated with the percentage of correct classification, kappa statistics and ROC area. Numerical results show that Random forest-bagging and artificial neural networks-bagging hybrid methods have the best performance among all models.

Keywords

• Box-office Revenue,Artificial Neural Networks,Support Vector Machines,Decision Trees,Machine Learning

References

1. Abou-Nasr, M., Lessmann, S., Stahlbock, R., & Weiss, G. M. (Eds.). (2014). Real world data mining applications (Vol. 17). Springer.
2. Aggarwal, C. C. (2015). Data mining: the textbook. Springer.
3. Akçetin, E., & Çelik, U. (2014). İstenmeyen Elektronik Posta (Spam) Tespitinde Karar Ağacı Algoritmalarının Performans Kıyaslaması. Journal of Internet Applications & Management/İnternet Uygulamaları ve Yönetimi Dergisi, 5(2).
4. Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
5. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
6. Castillo, P. A., Mora, A. M., Faris, H., Merelo, J. J., García-Sánchez, P., Fernández-Ares, A. J., … García-Arenas, M. I. (2016). Applying Computational Intelligence Methods for Predicting the Sales of Newly Published Books in a Real Editorial Business Management Environment. Knowledge-Based Systems, 115, 133–151.
7. Chou, J. S., Tsai, C. F., Pham, A. D., & Lu, Y. H. (2014). Machine learning in concrete strength simulations: Multi-nation data analytics. Construction and Building Materials, 73, 771–780.
8. Cichosz, P. (2014). Data Mining Algorithms: Explained Using R. John Wiley & Sons.

9. Delen, D., Sharda, R., & Kumar, P. (2007). Movie forecast Guru: A Web-based DSS for Hollywood managers. Decision Support Systems, 43(4), 1151–1170.
10. Erdal, H. I., Karakurt, O., & Namli, E. (2013). High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform. Engineering Applications of Artificial Intelligence, 26(4), 1246–1254.
11. Ghiassi, M., Lio, D., & Moon, B. (2015). Pre-production forecasting of movie revenues with a dynamic artificial neural network. Expert Systems with Applications, 42(6), 3176–3193.
12. Han, J. & Kamber, M. (2006). Data Mining: Concepts and Techniques, San Francisco: Morgan Kaufmann Publishers.
13. Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2003). A practical guide to support vector classification. Department of Computer Science, National Taiwan University.
14. Hur, M., Kang, P., & Cho, S. (2016). Box-office forecasting based on sentiments of movie reviews and Independent subspace method. Information Sciences, 372, 608–624.
15. Kalmegh, S. (2015). Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News. International Journal of Innovative Science, Engineering & Technology, 2(2), 438-446.
16. Quinlan, J. R. (1993). C4. 5: Programs for machine learning. California: Morgan Kauffmann Publishers.
17. Rokach, L., & Maimon, O. (2014). Data mining with decision trees: theory and applications. World scientific.
18. Sasaki, Y. (2007). The truth of the F-measure. Teach Tutor mater, 1(5).
19. Sharda, R., & Delen, D. (2006). Predicting box-office success of motion pictures with neural networks. Expert Systems with Applications.
20. Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology, 49(11), 1225-1231.
21. Witten, I. H., Frank, E., & Hall, M. A., (2011). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
22. Wu, T. K., Huang, S. C., & Meng, Y. R. (2008). Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities. Expert Systems with Applications, 34(3), 1846-1856.
23. Zhang, L., Luo, J., & Yang, S. (2009). Forecasting box office revenue of movies with BP neural network. Expert Systems with Applications, 36(3 PART 2), 6580–6587.