Abstract
Typical learning classifier systems employ conjunctive logic rules for representing domain knowledge. The classifier XCS is an extension of LCS with the ability to learn boolean logic functions for data mining. However, most data mining problems cannot be expressed simply with boolean logic. Neural Logic Network (Neulonet) learning is a technique that emulates the complex human reasoning processes through the use of net rules. Each neulonet is analogous to a learning classifier that is rewarded using support and confidence measures which are often used in association-based classification. Empirical results shows promise in terms of generalization ability and the comprehensibility of rules.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chia, H.W.K., Tan, C.L.: Neural logic network learning using genetic programming. International Journal of Computational Intelligence and Applications (IJCIA) 1, 357–368 (2001)
Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan, Ann Arbor. Republished by MIT Press, Cambridge, MA, USA (1975, 1992)
Wilson, S.W.: Mining oblique data with XCS. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 158–174. Springer, Heidelberg (2001)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pp. 80–86 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chia, H.WK., Tan, CL. (2004). Confidence and Support Classification Using Genetically Programmed Neural Logic Networks. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_97
Download citation
DOI: https://doi.org/10.1007/978-3-540-24855-2_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22343-6
Online ISBN: 978-3-540-24855-2
eBook Packages: Springer Book Archive