عنوان انگلیسی مقاله:
Comparing performance of ensemble methods in predicting movie box office revenue
ترجمه فارسی عنوان مقاله:
مقایسه عملکرد روش های گروه در پیش بینی درآمد گیشه فیلم
Sciencedirect - Elsevier - Heliyon, 6 (2017) e04260: doi:10:1016/j:heliyon:2020:e04260
While many business intelligence methods have been applied to predict movie box ofﬁce revenue, the studies using an ensemble approach to predict box ofﬁce revenue are almost nonexistent. In this study, we propose decision trees, k-nearest-neighbors (k-NN), and linear regression using ensemble methods and the prediction performance of decision trees based on random forests, bagging and boosting are compared with that of k-NN and linear regression based on bagging and boosting using the sample of 1439 movies. The results indicate that ensemble methods based on decision trees (random forests, bagging, boosting) outperform ensemble methods based on k-NN (bagging, boosting) in predicting box ofﬁce at week 1, 2, 3 after release. Decision trees using ensemble methods provide better prediction performance than ensemble methods based on linear regression analysis in the box ofﬁce at week 1 after release. This is explained by the results that after comparing the pre- diction performance between ensemble methods and non-ensemble methods. For decision tree methods, unlike the other methods, the prediction performance of ensemble methods is greater than that of non-ensemble methods. This shows that decision trees using ensemble methods provide better application effectiveness of ensemble methods than k-NN and linear regression analysis.
Keywords: Movie box office revenue | Ensemble methods | Prediction of box office revenue | Decision trees | Data analysis | Data analytics | Big data | Management | Business management