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.

翻译：在赌博研究中，分析特征基于对用户日常行为的数据监控量进行。在进行问题赌博检测时，现有数据集为构建基于机器学习的模型提供了相对丰富的分析特征。然而，考虑到实际应用中收集分析特征的复杂性和成本，使用较少特征进行精确检测将大幅降低数据收集成本。在本研究中，我们提出了一种深度神经网络PGN4，该网络在使用有限分析特征时表现良好。通过两个数据集的实验，我们发现PGN4在将特征数量从102个削减至5个时，性能仅出现轻微下降。此外，我们发现了两个数据集中前5个特征的共性。