MAKİNE ÖĞRENMESİ ALGORİTMALARI KULLANARAK GİŞE HASILATININ TAHMİNİ

Öz

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. 

Anahtar Kelimeler

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

FORECASTING OF BOX OFFICE REVENUE USING MACHINE LEARNING ALGORITHMS

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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