Multi-layer incremental induction
This paper describes a multi-layer incremental induction algorithm, MLII, which is linked to an existing nonincremental induction algorithm to learn incrementally from noisy data. MLII makes use of three operations: data partitioning, generalization and reduction. Generalization can either learn a set of rules from a (sub)set of examples, or refine a previous set of rules. The latter is achieved through a redescription operation called reduction: from a set of examples and a set of rules, we derive a new set of examples describing the behaviour of the rule set. New rules are extracted from these behavioral examples, and these rules can be seen as meta-rules, as they control previous rules in order to improve their predictive accuracy. Experimental results show that MLII achieves significant improvement on the existing nonincremental algorithm HCV used for experiments in this paper, in terms of rule accuracy.
Unable to display preview. Download preview PDF.
- [Kodratoff 1984]Kodratoff, Y. (1984). Learning complex structural descriptions from examples. Computer vision, graphics and image processing 27.Google Scholar
- [Langley 1996]Langley, P. (1996). Elements of Machine Learning. Morgan Kaufmann.Google Scholar
- [Leung 1992]Leung, K. T. (1992). Elementary Set Theory (3 Ed.). Hong Kong University Press.Google Scholar
- [Michalski 1984]Michalski, R. S. (1984). A theory and methodology for inductive learning. Artificial Intelligence 20(2).Google Scholar
- [Michalski 1985]Michalski, R. S. (1985). Knowledge repair mechanisms: Evolution versus revolution. In Proceedings of the Third International Machine Learning Workshop, 116–119. Rutgers University.Google Scholar
- [Murphy & Aha 95]Murphy, P.M. & Aha, D.W. (1995) UCI Repository of Machine Learning Databases, Machine-Readable Data Repository. University of California, Department of Information and Computer Science, Irvine, CA.Google Scholar
- [Quinlan 1993]Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann.Google Scholar
- [Ram 1990]Ram, A. (1990). Incremental learning of explantation patterns and their indices. In Proceedings of the Seventh International Conference on Machine Learning, 49–57. Morgan Kaufmann.Google Scholar
- [Schlimmer and Fisher 1986]Schlimmer, J. and Fisher, D. (1986). A case study of incremental concept induction. In Proceedings of the Fifth National Conference on Artifical Intelligence, pp. 496–501. Morgan Kaufmann.Google Scholar
- [Tim 1993]Tim, N. (1993, Feb). Discriminant generalization in logic program. Knowledge Representation and Organization in Machine Learning 14(3), 345–351.Google Scholar
- [Wu 1995]Wu, X. (1995). Knowledge Acquisition from Databases. Ablex.Google Scholar