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http://dx.doi.org/10.21219/jitam.2019.26.3.019

Predicting the Number of Movie Audiences Through Variable Selection Based on Information Gain Measure  

Park, Hyeon-Mock (Department of Bigdata, Chungbuk National University)
Choi, Sang Hyun (Department of MIS, Chungbuk National University)
Publication Information
Journal of Information Technology Applications and Management / v.26, no.3, 2019 , pp. 19-27 More about this Journal
Abstract
In this study, we propose a methodology for predicting the movie audience based on movie information that can be easily acquired before opening and effectively distinguishing qualitative variables. In addition, we constructed a model to estimate the number of movie audiences at the time of data acquisition through the configured variables. Another purpose of this study is to provide a criterion for categorizing success of movies with qualitative characteristics. As an evaluation criterion, we used information gain ratio which is the node selection criterion of C4.5 algorithm. Through the procedure we have selected 416 movie data features. As a result of the multiple linear regression model, the performance of the regression model using the variables selection method based on the information gain ratio was excellent.
Keywords
Information Gain Ratio; Machine Learning; Movie Audiences;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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