Detection of Problem Gambling with Less Features Using Machine Learning
  Methods
Yang Jiao Gloria Wong-Padoongpatt Mei Yang
Analytic features in gambling study are performed based on the amount of data
monitoring on user daily actions. While performing the detection of problem
gambling, existing datasets provide relatively rich analytic features for
building machine learning based model. However, considering the complexity and
cost of collecting the analytic features in real applications, conducting
precise detection with less features will tremendously reduce the cost of data
collection. In this study, we propose a deep neural networks PGN4 that performs
well when using limited analytic features. Through the experiment on two
datasets, we discover that PGN4 only experiences a mere performance drop when
cutting 102 features to 5 features. Besides, we find the commonality within the
top 5 features from two datasets.