Abstract
A preliminary investigation of the results produced by two cooperative learning strategies exploited in the system REGAL is reported. The objective is to produce a more efficient learning system. An extensive description about how to setup a suitable experimental setup is included. It is worthwhile to note that, in principle, these cooperative learning strategies could be applied to a pool of different learning systems.
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T.G. Dietterich and R.S. Michalski. A comparative review of selected methods for learning from examples. In J.G. Carbonell, R.S. Michalski, and T. Mitchell, editors, Machine Learning, an Artificial Intelligence Approach. Morgan Kaufmann, 1983.
R.S. King, S. Muggleton, R.A. Lewis, and M.J.E. Sternberg. Theories for mutagenecity: a study in first order and feature based induction. Artificial Intelligence, 74, 1995.
W. Lee, S. Stolfo, and K.W. Mok. Mining audit data to build intrusion detection models. In Knowledge discovery in databases 1998, pages 66–72, Fairfax, VA, 1998.
F. Neri. Comparing local search with respect to genetic evolution to detect intrusions in computer networks. In Congress on Evolutionary Computation 2000, pages 512–517, IEEE Press, 2000.
J.H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, Mi, 1975.
D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Ma, 1989.
K.A. De Jong, W.M. Spears, and F.D. Gordon. Using genetic algorithms for concept learning. Machine Learning, 13:161–188, 1993.
C.Z. Janikow. A knowledge intensive genetic algorithm for supervised learning. Machine Learning, 13:198–228, 1993.
A. Giordana and F. Neri. Search-intensive concept induction. Evolutionary Computation, 3(4):375–416, 1995.
J. Hekanaho. Background knowledge in ga-based concept learning. In 13th International Conference on Machine Learning, pages 234–242, Bari, Italy, 1996.
P. Husbands and F. Mill. A theoretical investigation of a parallel genetic algorithm. In Fourth International Conference on Genetic Algorithms, pages 264–270, Fairfax, VA, 1991. Morgan Kaufmann.
M. Potter. The Design and Analysis of a Computational Model of Cooperative Co-evolution. PhD thesis, Department of Computer Science. George Mason University, VA, 1997.
F. Neri. First Order Logic Concept Learning by means of a Distributed Genetic Algorithm. PhD thesis, Department of Computer Science. University of Torino, Italy, 1997.
J.L. Shapiro. Does data-mod co-evolution improve generalization performances of evolving learners? Lecture Notes in Computer Science, LNCS 1498:540–549, 1998.
R.E. Schapire. A brief introduction to boosting. pages 1401–1406, 1999.
T. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40:139–158, 2000.
R. Michalski, I. Mozetic, J. Hong, and N. Lavrac. The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. In Fifth National Conference on Artificial Intelligence, pages 1041–1045, Philadelphia, PA, 1986.
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, California, 1993.
F. Neri and L. Saitta. Exploring the power of genetic search in learning symbolic classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-18:1135–1142, 1996.
J.S. Schlimmer. Concept acquisition through representational adjustement. Technical Report TR 87-19, Dept. of Information and Computer Science, University of Californina, Irvine, CA, 1987.
R. Quinlan. Oversearching and layered search in empirical learning. In International Conference on Machine Learning, Lake Tahoe, CA, 1995.
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Neri, F. (2001). A Study on the Effect of Cooperative Evolution on Concept Learning. In: Boers, E.J.W. (eds) Applications of Evolutionary Computing. EvoWorkshops 2001. Lecture Notes in Computer Science, vol 2037. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45365-2_43
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DOI: https://doi.org/10.1007/3-540-45365-2_43
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