Yönetim Bilişim Sistemleri Dergisi

2630-550X 2630-550X

Dokuz Eylul University

MAKİNE ÖĞRENMESİ ALGORİTMALARI KULLANARAK GİŞE HASILATININ TAHMİNİ

FORECASTING OF BOX OFFICE REVENUE USING MACHINE LEARNING ALGORITHMS

Namlı

Özge Hüsniye

Özcan

Tuncay

12 20 2017

3 2 130 143 11 24 2017 12 31 2017

2015

Yeniefektler ve 3 boyutlu çekimler gibi güncel gelişmeler film endüstrisindekirekabeti arttırmaktadır. Film endüstrisindeki pahalı ve riskli yatırımlar içinüretim öncesi analizler giderek önem kazanmaktadır. Bu noktada, gişe hasılatıtahmini önemli bir araştırma konusu olmuştur. Bu bağlamda, bu çalışma gişehasılatı tahmini için makine öğrenmesi algoritmaları kullanarak bir yaklaşımsunmayı amaçlamaktadır. Geleneksel yapay zeka metotlarından yapay sinir ağlarıve destek vektör makineleri algoritmaları, karar ağaçları algoritmalarındanrastgele ağaç, rastgele orman ve C4.5 algoritmaları kullanılmıştır. Daha sonra,bu algoritmalar ile topluluk algoritmalarından torbalama algoritması kullanılarakmelez bir model önerilmiştir. Tahmin modelleri doğru sınıflandırma yüzdesi, kappaistatistiği, ROC alanı ile değerlendirilmiştir. Sayısal sonuçlar, rastgeleorman-torbalama ve yapay sinir ağları-torbalama melez metotlarının tüm modellerarasında en iyi performansa sahip olduğunu göstermektedir. 

Current developments such as new effects and 3Dshootings increase the competition in the movie industry. Pre-productionanalyzes are becoming more important for the expensive and risky investments inthe movie industry. At this point, the prediction of the box office revenue hasbecome an important research issue. In this context, this study aims to presentan approach using machine learning algorithms for box-office revenue prediction.Artificial neural networks and support vector machines algorithms as traditionalartificial intelligence methods and random trees, random forests and C4.5algorithms as decision tree algorithms are used. Later, a hybrid model is proposedusing these algorithms and the bagging algorithm from the ensemble algorithm. Predictionmodels are evaluated with the percentage of correct classification, kappastatistics and ROC area. Numerical results show that Random forest-bagging andartificial neural networks-bagging hybrid methods have the best performanceamong all models.

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

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

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