BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model

TL;DR

BehavDT is a novel decision tree model that incorporates user behavior generalization and context-awareness, improving the accuracy of predicting diverse smartphone user behaviors over traditional methods.

Contribution

This paper introduces BehavDT, a behavioral decision tree that balances generalized and context-specific decisions for enhanced user behavior prediction.

Findings

BehavDT outperforms traditional decision trees in accuracy.

The model effectively captures user preferences and context variations.

Experimental results validate the approach on real smartphone datasets.

Abstract

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.