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

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Abstract

Due to the vast and rapid increase in the size of data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. Rule learning methods, a special type of machine learning methods, can be used to build a rule based system as a special type of expert systems for both knowledge discovery and predictive modelling. A rule based system may be represented through different structures. The techniques for representing rules are known as rule representation, which is significant for knowledge discovery in relation to the interpretability of the model, as well as for predictive modelling with regard to efficiency in predicting unseen instances. This paper justifies the significance of rule representation and presents several existing representation techniques. Two types of novel networked topologies for rule representation are developed against existing techniques. This paper also includes complexity analysis of the networked topologies in order to show their advantages comparing with the existing techniques in terms of model interpretability and computational efficiency.

References

  • [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] 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, 2012

  • [3] P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, New Jersey: Pearson Education, 2006

  • [4] T. Mitchell, Machine Learning, New York: McGraw Hill, 1997

  • [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. 209–230

  • [6] C. M. Higgins, Classification and Approximation with Rule Based Networks, Pasadena, California, 1993.

  • [7] A. M. Uttley, The Design of Conditional Probability Computers, Information and control, vol. 2, pp. 1–24, 1959

  • [8] I. Kononenko, Bayesain Neual Networks, Biological Cybernetics, vol. 61, pp. 361–370, 1989

  • [9] F. Rosenblatt, Principles of Neurodynamics: Perceptron and the Theory of Brain Mechanisms, Washington, DC: Spartan Books, 1962

  • [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] A. V. Aho, J. E. Hopcraft and J. D. Ullman, Data Structures and Algorithms, Amsterdam: Addison-Wesley, 1983

  • [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, 2014

  • [13] J. Furnkranz, Separate-and-Conquer rule learning, Artificial Intelligence Review, vol. 13, pp. 3–54, 1999

  • [14] R. Quinlan, C4.5: programs for machine learning, Morgan Kaufman, 1993

  • [15] J. Cendrowska, PRISM: an algorithm for inducing modular rules, International Journal of Man-Machine Studies, vol. 27, p. 349-370, 1987

  • [16] X. Deng, A covering-based algorithm for classification: PRISM, SK, 2012

  • [17] A. Gegov, Complexity Management in Fuzzy Systems, Berlin: Springer, 2007

  • [18] T. J. Ross, Fuzzy Logic with Engineering Applications, West Sussex: John Wiley & Sons Ltd, 2004

  • [19] S. G. Simpson, Mathematical Logic, PA, 2013

  • [20] A. Holland, Lecture 2: Rules based systems, 2010

  • [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, 2015

  • [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

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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