1. bookVolume 7 (2017): Issue 2 (April 2017)
Journal Details
License
Format
Journal
First Published
30 Dec 2014
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4 times per year
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English
access type Open Access

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

Published Online: 23 Feb 2017
Page range: 111 - 123
Journal Details
License
Format
Journal
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English

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.

Keywords

[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, 1996Search in Google Scholar

[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, 2012Search in Google Scholar

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

[4] T. Mitchell, Machine Learning, New York: McGraw Hill, 1997Search in Google Scholar

[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. 209–230Search in Google Scholar

[6] C. M. Higgins, Classification and Approximation with Rule Based Networks, Pasadena, California, 1993.Search in Google Scholar

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

[8] I. Kononenko, Bayesain Neual Networks, Biological Cybernetics, vol. 61, pp. 361–370, 1989Search in Google Scholar

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

[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, 1988Search in Google Scholar

[11] A. V. Aho, J. E. Hopcraft and J. D. Ullman, Data Structures and Algorithms, Amsterdam: Addison-Wesley, 1983Search in Google Scholar

[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, 2014Search in Google Scholar

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

[14] R. Quinlan, C4.5: programs for machine learning, Morgan Kaufman, 1993Search in Google Scholar

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

[16] X. Deng, A covering-based algorithm for classification: PRISM, SK, 2012Search in Google Scholar

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

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

[19] S. G. Simpson, Mathematical Logic, PA, 2013Search in Google Scholar

[20] A. Holland, Lecture 2: Rules based systems, 2010Search in Google Scholar

[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, 2015Search in Google Scholar

[22] H. Liu, A. Gegov and M. Cocea, Rule Based Systems for Big Data: A Machine Learning Approach, 1 ed., vol. 13, Switzerland: Springer, 2016Search in Google Scholar

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