Abstract
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).
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
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
Ahila R, Sadasivam V (2014) Performance enhancement of extreme learning machine for power system disturbances classification. Soft Comput 18(2):239–253
Badr S, Bargiela A (2011) Case study of inaccuracies in the granulation of decision trees. Soft Comput 15(6):1129–1136
Bifet A, Holmes G, Kirkby R, Pfahringe B (2010) MOA: massive online analysis. J Mach Learn Res 11:1601–1604
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
Bifet A, Kirkby R (2009) Data stream mining a practical approach. University of Waikata
Bouchachia A (2011) Fuzzy classification in dynamic environments. Soft Comput 15(5):1009–1022
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
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46
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
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
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
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
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
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
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
Khanli LM, Analoui M (2009) Active grid information server for grid computing. J Supercomput 50(1):19–35
Kranen P (2011) Anytime algorithms for stream data mining. Doctoral Theses, RWTH Aachen University
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
Luengo J, Sez JA, Herrera F (2012) Missing data imputation for fuzzy rule-based classification systems. Soft Comput 16(5):863–881
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
Mitra S, Pal S, Mitra P (2002) Data mining in soft computing framework: a survery. IEEE Trans Neural Netw 13(1):3–14
Nauck DD (2004) Neuro-fuzzy learning with symbolic and numeric data. Soft Comput 8(6):383–396
Olaru C, Wehenkel L (2003) A complete fuzzy decision tree technique. Fuzzy Sets Syst 138(2):221–254
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
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
Weiqing J (2005) Fuzzy classification based on fuzzy association rule mining. Ph.D thessis of Philosophy, Graduate Faculty of North Carolina State University
Wenerstrom B, Giraud-Carrier C (2007) Temporal data mining in dynamic feature spaces. In: Proceedings of 6th international conference data mining, pp 1141–1145
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
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
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
Zhai JH (2011) Fuzzy decision tree based on fuzzy-rough technique. Soft Comput 15(6):1087–1096
Zhang D (2012) A new approach and system for attentive mobile learning based on seamless migration. Appl Intell 36(1):75–89
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
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
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
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
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Isazadeh, A., Mahan, F. & Pedrycz, W. MFlexDT: multi flexible fuzzy decision tree for data stream classification. Soft Comput 20, 3719–3733 (2016). https://doi.org/10.1007/s00500-015-1733-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-015-1733-2