Skip to main content
SpringerLink
Log in

THRFuzzy: Tangential holoentropy-enabled rough fuzzy classifier to classification of evolving data streams

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means (FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. ROSS G J, TASOULIS D K, ADAMS N M. Nonparametric monitoring of data streams for changes in location and scale [J]. Technometr, 2012, 53(4): 379–389.

    Article  MathSciNet  Google Scholar 

  2. BRZEZINSKI D, STEFANOWSKI J. Reacting to different types of concept drift: the accuracy updated ensemble algorithm [J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(1): 81–94.

    Article  Google Scholar 

  3. ZHU X, ZHANG P, LIN X, SHI Y. Active learning from stream data using optimal weight classifier ensemble [J]. IEEE Trans System, Man, Cybernetics, Part B: Cybernetics, 2010, 40(4): 1–15.

    Google Scholar 

  4. BIFET A, HOLMES G, PFAHRINGER B, KIRKBY R, GAVALDA R. New ensemble methods for evolving data streams [C]// Proc 15th ACM SIGKDD Int’l Conf Knowledge Discovery and Data Mining (KDD), 2009: 139–148.

    Chapter  Google Scholar 

  5. MASUD M, GAO J, KHAN L, HAN J, THURAISINGHAM B. Classification and novel class detection in concept-drifting data streams under time constraints [J]. IEEE Trans Knowledge and Data Eng., 2011, 23(6): 859–874.

    Article  Google Scholar 

  6. ZHANG Peng, ZHOU Chuan, WANG Peng, GAO B J, ZHU Xing-quan, GUO Li. E-Tree: An efficient indexing structure for ensemble models on data streams [J]. IEEE Transactions on Knowledge and Data engineering, 2015, 27(2): 461–474.

    Article  Google Scholar 

  7. RUTKOWSKI L, JAWORSKI M, PIETRUCZUK L, DUDA P. Decision trees for mining data streams based on the gaussian approximation [J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(1): 108–119.

    Article  MATH  Google Scholar 

  8. FAN W. Systematic data selection to mine concept-drifting data streams [C]// Proc ACM SIGKDD 10th Int’l Conf Knowledge Discovery and Data Mining. 2004: 128–137.

    Google Scholar 

  9. MENA-TORRES D, AGUILAR-RUIZ J S. A similarity-based approach for data stream classification [J]. Expert Systems with Applications, 2014, 41: 4224–4234.

    Article  Google Scholar 

  10. HULTEN G, SPENCER L, DOMINGOS P. Mining time-changing data streams [C]// Proc ACM SIGKDD Seventh Int’l Conf Knowledge Discovery and Data Mining. 2001: 97–106.

    Chapter  Google Scholar 

  11. GAO J, FAN W, HAN J. On appropriate assumptions to mine data streams [C]// Proc IEEE Seventh Int’l Conf Data Mining (ICDM). 2007: 143–152.

    Chapter  Google Scholar 

  12. KOLTER J, MALOOF M. Using additive expert ensembles to cope with concept drift [C]// Proc 22nd Int’l Conf Machine Learning (ICML). 2005: 449–456.

    Chapter  Google Scholar 

  13. WANG H, FAN W, YU P S, HAN J. Mining concept-drifting data streams using ensemble classifiers [C]// Proc ACM SIGKDD Ninth Int’l Conf Knowledge Discovery and Data Mining, 2003: 226–235.

    Chapter  Google Scholar 

  14. KATAKIS I, TSOUMAKAS G, VLAHAVAS I. On the utility of incremental feature selection for the classification of textual data streams [C]// Advances in Informatics. New York, NY, USA: Springer-Verlag, 2005: 338–348.

    Google Scholar 

  15. GOMES J B, SOUSA P A C, MENASALVAS E. Tracking recurrent concepts using context [C]// Proc 7th Int Conf RSCTC. 2010: 168–177.

    Google Scholar 

  16. GAMA J, KOSINA P. Tracking recurring concepts with metalearners [C]// In Proc 14th Portuguese Conf Artif Intell. 2009: 423.

    Chapter  Google Scholar 

  17. YANG Y, WU X, ZHU X. Mining in anticipation for concept change: Proactive-reactive prediction in data streams [J]. Data Mining Knowl Discovery, 2006, 13(3): 261–289.

    Article  MathSciNet  Google Scholar 

  18. HARSHE M S MANASI V. Outlier detection using weighted holoentropy [J]. International Journal of Advances in Engineering Science and Technology, 2016, 5(1): 52–58.

    Google Scholar 

  19. PAWLAK Z. Rough sets [J]. International Journal of Parallel Programming, 1982, 11(5): 341–356.

    MATH  Google Scholar 

  20. ZADEH L. Fuzzy sets. [M]. Fuzzy models for pattern recognition: Methods that search for structures in data. NY: IEEE Press, 1992.

    Google Scholar 

  21. THAKUR P, LINGAM C. Generalized spatial kernel based fuzzy c-means clustering algorithm for image segmentation [J]. International Journal of Science and Research (IJSR), 2013, 2(5): 165–169.

    Google Scholar 

  22. ALIPPI C, LIU De-rong, ZHAO Dong-bin, LI Bu. Detecting and reacting to changes in sensing units: The active classifier case [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2013, 44(3): 353–362.

    Article  Google Scholar 

  23. GOMES J B, GABER M M, PEDRO A, SOUSA C. Mining recurring concepts in a dynamic feature space [J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(1): 95–110.

    Article  Google Scholar 

  24. MASUD M M, CHEN Q, KHAN L, AGGARWAL C C, GAO J, HAN J W, SRIVASTAVA A, OZA N C. Classification and adaptive novel class detection of feature-evolving data streams [J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(7): 1484–1497.

    Article  Google Scholar 

  25. ABDULSALAM H, SKILLICORN D B, MARTIN P. Classification using streaming random forests [J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(1): 22–36.

    Article  Google Scholar 

  26. LICHMAN M. UC Irvine Machine Learning Repository [EB/OL]. [2016−01−10]. http://archive. ics.uci. edu/ml/datasets.html.

Download references

Acknowledgment

This work is supported by proposal No. OSD/BCUD/392/197 Board of Colleges and University Development, Savitribai Phule Pune University, Pune.

Author information

Authors and Affiliations

  1. Department of CSE, Veltech Dr. RR & Dr. SR Technical University, Chennai, India

    Jagannath E. Nalavade & T. Senthil Murugan

Authors
  1. Jagannath E. Nalavade
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Senthil Murugan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jagannath E. Nalavade.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nalavade, J.E., Murugan, T.S. THRFuzzy: Tangential holoentropy-enabled rough fuzzy classifier to classification of evolving data streams. J. Cent. South Univ. 24, 1789–1800 (2017). https://doi.org/10.1007/s11771-017-3587-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-017-3587-5

Key words

Search

Navigation