Analysing Content Ratings of Google Apps with Ensemble Learning
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Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
TUBITAK
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Google Play was launched under the name of Android Market and made its reputation known all over the world. The mobile application market, which is a package manager developed by Google for Android users, contains applications that appeal to many areas and age ranges. Applications are spread over a wide range of uses. Thus, the amount and size of the data increased, and this situation began to attract the attention of researchers. The excessive increase in the number of applications makes it difficult for parents to follow up on the content. To provide the content rating of applications on Google Play, it is needed to be classified by machine learning methods. In this study, content rating classification was made by analyzing “Category, Rating, Reviews, Size, Installs, Type, Genres, Last Updated, Current Version, Android Version” features of 10757 applications on Google Play, Ensemble Learning methods (Adaboost, Bagging, Random Forest, Stacking), Logistic Regression, Artificial Neural Network, K-Nearest Neighbors algorithms. © 2022, TUBITAK. All rights reserved.
Açıklama
Anahtar Kelimeler
classification, content rating, Ensemble learning, google apps
Kaynak
El-Cezeri Journal of Science and Engineering
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
9
Sayı
3
