BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model
This work addresses the need for user-centric predictive models in context-aware environments, though it appears incremental as it builds on decision trees with added generalization.
The paper tackles the problem of building context-aware predictive models for user behavior with smartphones by proposing BehavDT, a behavioral decision tree that generalizes decisions based on user preferences while handling exceptions, and experimental results show it is more effective than traditional approaches in predicting diverse behaviors.
This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used to build a data-driven predictive model. The traditional decision tree model typically creates a number of leaf nodes as decision nodes that represent context-specific rigid decisions, and consequently may cause overfitting problem in behavior modeling. However, in many practical scenarios within the context-aware environment, the generalized outcomes could play an important role to effectively capture user behavior. In this paper, we propose a behavioral decision tree, "BehavDT" context-aware model that takes into account user behavior-oriented generalization according to individual preference level. The BehavDT model outputs not only the generalized decisions but also the context-specific decisions in relevant exceptional cases. The effectiveness of our BehavDT model is studied by conducting experiments on individual user real smartphone datasets. Our experimental results show that the proposed BehavDT context-aware model is more effective when compared with the traditional machine learning approaches, in predicting user diverse behaviors considering multi-dimensional contexts.