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Hybrid Splitting Criteria

  • Leszek RutkowskiEmail author
  • Maciej Jaworski
  • Piotr Duda
Chapter
Part of the Studies in Big Data book series (SBD, volume 56)

Abstract

In the previous chapters, various types of splitting criteria were proposed. Each of the presented criteria is constructed using one specific impurity measure (or, more precisely, the corresponding split measure function). Therefore we will refer to such criteria as ‘single’ splitting criteria. The experiments conducted in Chap.  5 demonstrate that various single splitting criteria have their own advantages and drawbacks. Based on this observation a new kind of splitting criteria can be proposed, which combine together two different single criteria in a heuristic manner. We refer to them as hybrid splitting criteria. In this chapter, we discuss premises which demonstrate that such an approach may lead to satisfactory results.

References

  1. 1.
    Wozniak, M.: Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination. Springer Publishing Company, Berlin (2013). IncorporatedGoogle Scholar
  2. 2.
    Woźniak, M., Grana, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014). Special Issue on Information Fusion in Hybrid Intelligent Fusion SystemsGoogle Scholar
  3. 3.
    Gogte, P.S., Theng, D.P.: Hybrid ensemble classifier for stream data. In: 2014 Fourth International Conference on Communication Systems and Network Technologies, April 2014, pp. 463–467 (2014)Google Scholar
  4. 4.
    Kim, H., Madhvanath, S., Sun, T.: Hybrid active learning for non-stationary streaming data with asynchronous labeling. In: 2015 IEEE International Conference on Big Data (Big Data), October 2015, pp. 287–292 (2015)Google Scholar
  5. 5.
    Kim, K., Hong, J.-S.: A hybrid decision tree algorithm for mixed numeric and categorical data in regression analysis. Pattern Recogn. Lett. 98, 39–45 (2017)CrossRefGoogle Scholar
  6. 6.
    Chen, H.M., Wang, H.C., Chang, Y.C., Chai, J.W., Chen, C.C.C., Hung, C.L., Chang, C.I.: A supervised hybrid classifier for brain tissues and white matter lesions on multispectral MRI. In: 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks, 2017 11th International Conference on Frontier of Computer Science and Technology, 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC), June 2017, pp. 375–379 (2017)Google Scholar
  7. 7.
    Datta, S., Dev, V.A., Eden, M.R.: Hybrid genetic algorithm-decision tree approach for rate constant prediction using structures of reactants and solvent for Diels-Alder reaction. Comput. Chem. Eng. 106, 690–698 (2017)CrossRefGoogle Scholar
  8. 8.
    Zhou, Z.-H., Chen, Z.-Q.: Hybrid decision tree. Knowl. Based Syst. 15(8), 515–528 (2002)CrossRefGoogle Scholar
  9. 9.
    Gama, J.: Accurate decision trees for mining high-speed data streams. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 523–528. ACM Press (2003)Google Scholar
  10. 10.
    Farid, D.M., Zhang, L., Mofizur Rahman, C., Hossain, M.A., Strachan, R.: Hybrid decision tree and naive Bayes classifiers for multi-class classification tasks. Expert Syst. Appl. 41(4), Part 2, 1937–1946 (2014)Google Scholar
  11. 11.
    Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: A new method for data stream mining based on the misclassification error. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1048–1059 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Jaworski, M., Rutkowski, L., Pawlak, M.: Hybrid splitting criterion in decision trees for data stream mining. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing, pp. 60–72. Springer International Publishing, Cham (2016)Google Scholar
  13. 13.
    Jaworski, M., Duda, P., Rutkowski, L.: New splitting criteria for decision trees in stationary data streams. IEEE Trans. Neural Netw. Learn. Syst. 29, 2516–2529 (2018)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Li, F., Zhang, X., Zhang, X., Du, C., Xu, Y., Tian, Y.-C.: Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets. Inf. Sci. 422, 242–256 (2018)CrossRefGoogle Scholar
  15. 15.
    Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80 (2000)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Leszek Rutkowski
    • 1
    • 2
    Email author
  • Maciej Jaworski
    • 1
  • Piotr Duda
    • 1
  1. 1.Institute of Computational IntelligenceCzestochowa University of TechnologyCzęstochowaPoland
  2. 2.Information Technology InstituteUniversity of Social SciencesLodzPoland

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