MFlexDT: multi flexible fuzzy decision tree for data stream classification


In many real-world applications, instances (data) arrive sequentially in the form of streams. Processing such data poses challenges to machine learning. While adhering to on-line learning strategies, in this paper we extend the Flexible Fuzzy Decision Tree (FlexDT) algorithm with multiple partitioning that makes it possible to carry out automatic on-line fuzzy data classification. The proposed method is aimed to balance accuracy and tree size in data stream mining. The objective of the classification problem is to predict the true class of each incoming instances in real time. In terms of evaluation of the method, accuracy, tree depth, and the learning time are significant factors influencing the performance. A series of experiments demonstrate that the proposed method produces optimal trees for both numeric and nominal features (variables).

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  1. Afsari F, Eftekhari M, Eslami E, Woo P-Y (2013) Interpretability-based fuzzy decision tree classifier a hybrid of the subtractive clustering and the multi-objective evolutionary algorithm. Soft Comput 17(9):1673–1686

    Article  Google Scholar 

  2. Ahila R, Sadasivam V (2014) Performance enhancement of extreme learning machine for power system disturbances classification. Soft Comput 18(2):239–253

    Article  Google Scholar 

  3. Badr S, Bargiela A (2011) Case study of inaccuracies in the granulation of decision trees. Soft Comput 15(6):1129–1136

    Article  Google Scholar 

  4. Bifet A, Holmes G, Kirkby R, Pfahringe B (2010) MOA: massive online analysis. J Mach Learn Res 11:1601–1604

    Google Scholar 

  5. Bifet A, Holmes G, Pfahringer B, Kirkby R, Gavald‘a R (2009) New ensemble methods for evolving data streams. In: Proceedings of the 15th ACMSIGKDD international conference on knowledge discovery and data mining (KDD’09). ACM, Paris, pp 139–147

  6. Bifet A, Kirkby R (2009) Data stream mining a practical approach. University of Waikata

  7. Bouchachia A (2011) Fuzzy classification in dynamic environments. Soft Comput 15(5):1009–1022

    Article  Google Scholar 

  8. Browne A, Hudson B, Whitley D, Ford M, Picton P (2004) Biological data mining with neural networks: implementation and application of a flexible decision tree extraction algorithm to genomic problem domains. Neurocomputing J 57:275–293

    Article  Google Scholar 

  9. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46

    Article  Google Scholar 

  10. Evans L, Lohse N, Summers M (2013) A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information. In: Expert systems with applications, vol 40, issue 16, pp 6412–6426

  11. Gama J, Gaber M (2007) Medhat., predictive learning in sensor networks. In: Chapter 10 of learning from data streams—Processing techniques in sensor networks. Springer, Berlin, pp 143–164

  12. Gomes JB, Gaber MM, Sousa PAC, Menasalvas E (2014) Mining recurring concepts in a dynamic feature space. Neural Netw Learn Syst IEEE Trans 25(1):95–110

    Article  Google Scholar 

  13. Hamzeia Shah GH, Mulvaneya DJ (1999) On-line learning of fuzzy decision trees for global path planning. Eng Appl Artif Intell 12(1):93–109

    Article  Google Scholar 

  14. Hashemi S, Yang Y (2009) Flexible decision tree for data stream classification in the presence of concept change, noise and missing values. Data Min Knowl Discov J 19:95–131

    MathSciNet  Article  Google Scholar 

  15. Hoeglinger S, Pears R (2007) Use of Hoeffding trees in concept based data stream mining. In: Third International conference on information and automation for sustainability, pp 57–62

  16. Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams. In: Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD), pp 97–106

  17. Khanli LM, Analoui M (2009) Active grid information server for grid computing. J Supercomput 50(1):19–35

  18. Kranen P (2011) Anytime algorithms for stream data mining. Doctoral Theses, RWTH Aachen University

  19. Li D, Gu H, Zhang L (2013) A hybrid genetic algorithm–fuzzy c-means approach for incomplete data clustering based on nearest-neighbor intervals. Soft Comput 17(10):1787–1796

    Article  Google Scholar 

  20. Luengo J, Sez JA, Herrera F (2012) Missing data imputation for fuzzy rule-based classification systems. Soft Comput 16(5):863–881

    Article  Google Scholar 

  21. Milln-Giraldo M, Salvador Snchez J, Javier Traver V (2011) On-line learning from streaming data with delayed attributes: a comparison of classifiers and strategies. Neural Comput Appl 20(7):935–944

    Article  Google Scholar 

  22. Mitra S, Pal S, Mitra P (2002) Data mining in soft computing framework: a survery. IEEE Trans Neural Netw 13(1):3–14

  23. Nauck DD (2004) Neuro-fuzzy learning with symbolic and numeric data. Soft Comput 8(6):383–396

    Article  Google Scholar 

  24. Olaru C, Wehenkel L (2003) A complete fuzzy decision tree technique. Fuzzy Sets Syst 138(2):221–254

  25. Orriols-Puig A, Casillas J (2011) Fuzzy knowledge representation study for incremental learning in data streams and classification problems. Soft Comput 15(12):2389–2414

    Article  Google Scholar 

  26. Sugumaran V, Nair B (2010) Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump. Mech Syst Signal Process J 24(6):1887–1906

    Article  Google Scholar 

  27. Weiqing J (2005) Fuzzy classification based on fuzzy association rule mining. Ph.D thessis of Philosophy, Graduate Faculty of North Carolina State University

  28. Wenerstrom B, Giraud-Carrier C (2007) Temporal data mining in dynamic feature spaces. In: Proceedings of 6th international conference data mining, pp 1141–1145

  29. Yang H, Fong S (2011) Moderated VFDT in stream mining using adaptive tie threshold and incremental pruning. In: Proceedings of the 13th international conference on data warehousing and knowledge discovery (DaWaK’11). Springer, Toulouse, pp 471–483

  30. Yang H, Fong S (2013) Incremental optimization mechanism for constructing a decision tree in data stream mining. Math Probl Eng 2013. doi:10.1155/2013/580397

  31. Yao Z, Lou G, Song X, Zhou Y (2010) On-line fault diagnosis study for roller bearing based on fuzzy fault tree. In: Informatics in control, automation and robotics (CAR). Proceeding of 2010 2nd international Asia conference. China, pp 182–185

  32. Zhai JH (2011) Fuzzy decision tree based on fuzzy-rough technique. Soft Comput 15(6):1087–1096

    Article  Google Scholar 

  33. Zhang D (2012) A new approach and system for attentive mobile learning based on seamless migration. Appl Intell 36(1):75–89

    Article  Google Scholar 

  34. Zhang D, Li G, Zheng K (2014) An energy-balanced routing method based on forward-aware factor for wireless sensor network. IEEE Trans Ind Inf 10(1):766–773

    Article  Google Scholar 

  35. Zhang D, Wang X, Song X (2014) A novel approach to mapped correlation of ID for RFID anti-collision. IEEE Trans Serv Comput 7(4):741–748

    MathSciNet  Article  Google Scholar 

  36. Zhang D, Dan Zhang X (2012) Design and implementation of embedded un-interruptible power supply system (EUPSS) for web-based mobile application. Enterprise IS 6(4):473–489

    Article  Google Scholar 

  37. Zhang D, Zhu Y (2012) A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the internet of things (IOT). Comput Math Appl 64(5):1044–1055

    Article  Google Scholar 

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Correspondence to Farnaz Mahan.

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Communicated by V. Loia.

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Isazadeh, A., Mahan, F. & Pedrycz, W. MFlexDT: multi flexible fuzzy decision tree for data stream classification. Soft Comput 20, 3719–3733 (2016).

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  • Classification
  • Stream data
  • Multiple partitioning
  • Flexible fuzzy decision tree