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

  • Iqbal H. SarkerEmail author
  • Alan Colman
  • Jun Han
  • Asif Irshad Khan
  • Yoosef B. Abushark
  • Khaled Salah


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.


Mobile data analytics Machine learning Classification Decision tree Context-aware computing User behavior modeling Predictive analytics Personalization Intelligent services and systems 



  1. 1.
    Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66Google Scholar
  2. 2.
    Eagle N, Pentland AS (2006) Reality mining: sensing complex social systems. Personal and ubiquitous computing 10(4):255–268CrossRefGoogle Scholar
  3. 3.
    El Khaddar MA, Boulmalf M (2017) Smartphone: the ultimate iot and ioe device. Smartphones from an Applied Research Perspective, pp 137Google Scholar
  4. 4.
    Zulkernain S et al (2010) A mobile intelligent interruption management system. J UCS 16(15):2060–2080Google Scholar
  5. 5.
    Eibe F, Witten IH (1998) Generating accurate rule sets without global optimizationGoogle Scholar
  6. 6.
    Freitas AA (2000) Understanding the crucial differences between classification and discovery of association rules: A position paper. ACM SIGKDD Explorations Newsletter 2(1):65–69CrossRefGoogle Scholar
  7. 7.
    Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, AmsterdamzbMATHGoogle Scholar
  8. 8.
    Holte RC (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11(1):63–90zbMATHCrossRefGoogle Scholar
  9. 9.
    Hong J, Suh E-H, Kim J, Kim SY (2009) Context-aware system for proactive personalized service based on context history. Expert Syst Appl 36(4):7448–7457CrossRefGoogle Scholar
  10. 10.
    John GH, Langley P (1995) Estimating continuous distributions in bayesian classifiers. In: Proceedings of the 11th conference on uncertainty in artificial intelligence, pp 338–345. Morgan Kaufmann Publishers Inc.Google Scholar
  11. 11.
    Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK (2001) Improvements to platt’s smo algorithm for svm classifier design. Neural Comput 13(3):637–649zbMATHCrossRefGoogle Scholar
  12. 12.
    Le Cessie S, Van Houwelingen JC (1992) Ridge estimators in logistic regression. Journal of the Royal Statistical Society: Series C (Applied Statistics) 41(1):191–201zbMATHGoogle Scholar
  13. 13.
    Lee W-P (2007) Deploying personalized mobile services in an agent-based environment. Expert Systems with Applications, 32(4)CrossRefGoogle Scholar
  14. 14.
    Veljko P, Mirco M (2014) Interruptme: designing intelligent prompting mechanisms for pervasive applications. In: UbiComp, pp 897–908. ACMGoogle Scholar
  15. 15.
    Pielot M (2014) Large-scale evaluation of call-availability prediction. In: Proceedings of the international joint conference on pervasive and ubiquitous computing, pp 933–937. ACMGoogle Scholar
  16. 16.
    Pielot M, De Oliveira R, Kwak H, Oliver N (2014) Didn’t you see my message?: predicting attentiveness to mobile instant messages. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 3319–3328. ACMGoogle Scholar
  17. 17.
    Quinlan RJ (1986) Induction of decision trees. Mach Learn 1(1):81–106Google Scholar
  18. 18.
    Quinlan RJ (1993) C4.5: Programs for machine learning. Machine LearningGoogle Scholar
  19. 19.
    Sarker I (2018) Mobile data science: Towards understanding data-driven intelligent mobile applications. EAI Endorsed Transactions on Scalable Information Systems 5(19):e4Google Scholar
  20. 20.
    Sarker IH (2018) Behavminer: Mining user behaviors from mobile phone data for personalized services. In: Proceedings of the 2018 IEEE international conference on pervasive computing and communications (PerCom 2018). IEEE, AthensGoogle Scholar
  21. 21.
    Sarker IH (2018) Understanding the role of data-centric social context in personalized mobile applications. EAI Endorsed Transactions on Context-aware Systems and Applications 5(15):e1CrossRefGoogle Scholar
  22. 22.
    Sarker IH (2019) Context-aware rule learning from smartphone data: survey, challenges and future directions. J Big Data 6 (1):95CrossRefGoogle Scholar
  23. 23.
    Sarker IH (2019) A machine learning based robust prediction model for real-life mobile phone data. Internet of Things 5:180–193CrossRefGoogle Scholar
  24. 24.
    Sarker IH, Colman A, Han J (2019) Recencyminer: mining recency-based personalized behavior from contextual smartphone data. J Big Data 6(1):49CrossRefGoogle Scholar
  25. 25.
    Sarker IH, Colman A, Han J, Kayes ASM, Watters P (2019) Calbehav: A machine learning based personalized calendar behavioral model using time-series smartphone data. Comput J :1–16Google Scholar
  26. 26.
    Sarker IH, Colman A, Kabir MA, Han J (2017) Individualized time-series segmentation for mining mobile phone user behavior. Comput J 61(3):349–368CrossRefGoogle Scholar
  27. 27.
    Sarker IH, Kabir MA, Colman A, Han J (2017) An approach to modeling call response behavior on mobile phones based on multi-dimensional contexts. In: Proceedings of the 4th international conference on mobile software engineering and systems, pp 91–95. IEEE PressGoogle Scholar
  28. 28.
    Sarker IH, Kabir MA, Colman A, Han J (2017) An effective call prediction model based on noisy mobile phone data. In: Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers, pp 193–196. ACMGoogle Scholar
  29. 29.
    Sarker IH, Kayes ASM, Furhad H, Islam MM, Islam S (2019) E-miim: an ensemble-learning-based context-aware mobile telephony model for intelligent interruption management. AI and SOCIETY, pp 1–9Google Scholar
  30. 30.
    Sarker IH, Kayes ASM, Watters P (2019) Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. J Big Data 6(1):57CrossRefGoogle Scholar
  31. 31.
    Sarker IH, Salah K (2019) Appspred: Predicting context-aware smartphone apps using random forest learning. Internet of ThingsGoogle Scholar
  32. 32.
    Sheng S, Ling CX (2005) Hybrid cost-sensitive decision tree, knowledge discovery in databases. In: PKDD 2005, Proceedings of 9th European conference on principles and practice of knowledge discovery in databases. Lecture Notes in Computer Science, vol. 3721CrossRefGoogle Scholar
  33. 33.
    Witten IH, Frank E (2005) Data mining: Practical machine learning tools and techniques. Morgan, KaufmannzbMATHGoogle Scholar
  34. 34.
    Wu C-C, Chen Y-L, Liu Y-H, Yang X-Y (2016) Decision tree induction with a constrained number of leaf nodes. Appl Intell 45(3):673–685CrossRefGoogle Scholar
  35. 35.
    Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu S (2008) Philip Top 10 algorithms in data mining. Knowledge and information systems 14(1):1–37CrossRefGoogle Scholar
  36. 36.
    Zhu H et al (2014) Mining mobile user preferences for personalized context-aware recommendation. ACM Transactions on Intelligent Systems and Technology (TIST) 5(4):58CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Iqbal H. Sarker
    • 1
    • 2
    Email author
  • Alan Colman
    • 1
  • Jun Han
    • 1
  • Asif Irshad Khan
    • 3
  • Yoosef B. Abushark
    • 3
  • Khaled Salah
    • 4
  1. 1.Swinburne University of TechnologyMelbourneAustralia
  2. 2.Chittagong University of Engineering and TechnologyChittagongBangladesh
  3. 3.King Abdulaziz UniversityJeddahSaudi Arabia
  4. 4.Khalifa UniversityAbu DhabiUAE

Personalised recommendations