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Rule Based Networks: An Efficient and Interpretable Representation of Computational Models Cover

Rule Based Networks: An Efficient and Interpretable Representation of Computational Models

Open Access
|Feb 2017

References

  1. [1] U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, From Data Mining to Knowledge Discovery in Databases, AI Magazine, vol. 17, no. 3, pp. 37–54, 1996
  2. [2] F. Stahl and I. Jordanov, An overview of use of neural networks for data mining tasks, WIREs: Data Mining and Knowledge Discovery, pp. 193–208, 201210.1002/widm.1052
  3. [3] P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, New Jersey: Pearson Education, 2006
  4. [4] T. Mitchell, Machine Learning, New York: McGraw Hill, 1997
  5. [5] H. Liu, A. Gegov and F. Stahl, Unified Framework for Construction of Rule Based Classification Systems, in Inforamtion Granularity, Big Data and Computational Intelligence, vol. 8, W. Pedrycz and S. Chen, Eds., Springer, 2015, pp. 209–23010.1007/978-3-319-08254-7_10
  6. [6] C. M. Higgins, Classification and Approximation with Rule Based Networks, Pasadena, California, 1993.
  7. [7] A. M. Uttley, The Design of Conditional Probability Computers, Information and control, vol. 2, pp. 1–24, 195910.1016/S0019-9958(59)90058-0
  8. [8] I. Kononenko, Bayesain Neual Networks, Biological Cybernetics, vol. 61, pp. 361–370, 198910.1007/BF00200801
  9. [9] F. Rosenblatt, Principles of Neurodynamics: Perceptron and the Theory of Brain Mechanisms, Washington, DC: Spartan Books, 196210.21236/AD0256582
  10. [10] O. Ekeberg and A. Lansner, Automatic generation of internal representations in a probabilistic artificial neural network, in Proceedings of the First European Conference on Neural Networks, 1988
  11. [11] A. V. Aho, J. E. Hopcraft and J. D. Ullman, Data Structures and Algorithms, Amsterdam: Addison-Wesley, 1983
  12. [12] H. Liu, A. Gegov and F. Stahl, Categorization and Construction of Rule Based Systems, in 15th International Conference on Engineering Applications of Neural Networks, Sofia, Bulgaria, 201410.1007/978-3-319-11071-4_18
  13. [13] J. Furnkranz, Separate-and-Conquer rule learning, Artificial Intelligence Review, vol. 13, pp. 3–54, 199910.1023/A:1006524209794
  14. [14] R. Quinlan, C4.5: programs for machine learning, Morgan Kaufman, 1993
  15. [15] J. Cendrowska, PRISM: an algorithm for inducing modular rules, International Journal of Man-Machine Studies, vol. 27, p. 349-370, 198710.1016/S0020-7373(87)80003-2
  16. [16] X. Deng, A covering-based algorithm for classification: PRISM, SK, 2012
  17. [17] A. Gegov, Complexity Management in Fuzzy Systems, Berlin: Springer, 2007
  18. [18] T. J. Ross, Fuzzy Logic with Engineering Applications, West Sussex: John Wiley & Sons Ltd, 2004
  19. [19] S. G. Simpson, Mathematical Logic, PA, 2013
  20. [20] A. Holland, Lecture 2: Rules based systems, 2010
  21. [21] H. Liu, A. Gegov and M. Cocea, Network Based Rule Representation for Knowledge Discovery and Predictive Modelling, in IEEE International Conference on Fuzzy Systems, Istanbul, 201510.1109/FUZZ-IEEE.2015.7337807
  22. [22] H. Liu, A. Gegov and M. Cocea, Rule Based Systems for Big Data: A Machine Learning Approach, 1 ed., vol. 13, Switzerland: Springer, 2016
Language: English
Page range: 111 - 123
Published on: Feb 23, 2017
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2017 Han Liu, Alexander Gegov, Mihaela Cocea, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.