Abstract
One of the main and fundamental tasks of data mining is the automatic induction of classification rules from a set of examples and observations. A variety of methods performing this task have been proposed in the recent literature. Many comparative studies have been carried out in this field. However, the main common feature between thesemethods is that they are designedmanually. In the meanwhile, there have been some successful attempts to automatically design such methods using Grammar-based Genetic Programming (GGP). In this paper, we propose a different system called Automatic Grammar Genetic Programming (AGGP) that can evolve complete java program codes. These codes represent a rule induction algorithm that uses a grammar evolution technique that governs a Backus Naur Form grammar definition mapping to a program. To perform this task, we will use binary strings as inputs to the mapper along with the Backus Naur Form grammar. Such binary strings represent possible potential solutions resulting from the initialized component and Weka building blocks, this would ease the induction process and makes induced programs short. Experimental results prove the efficiency of the proposed method. It is also shown that, compared to some recent and similar manual techniques (Prism, Ripper, Ridor, OneRule) the proposed method outperforms such techniques.Abenchmark of well-known data sets is used for the sake of comparison.
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References
Beyer, H.G., Schwefel, H.P.: Evolution strategies: A comprehensive introduction. Natural Computing 1, 3–52 (2002)
Koza, J.: Genetic programming: on the programming of computers by means of natural selection. MIT Press (1992)
Pappa, G.L., Freitas, A.: Automating the Design of Data Mining Algorithms. Springer, Heidelberg (2010)
McKay, R., Hoai, N.X., Whigham, P.A., Shan, Y., O’Neill, M.: Grammar-based Genetic Programming: A review. Genetic Programming and Evolvable Machines, 365–396 (2010)
Wong, M.L., Leung, K.S.: Applying logic grammars to induce sub-functions in genetic programming. In: IEEE International Conference on Evolutionary Computation, vol. 2, pp. 737–740 (1995)
O’Neill, M., Hemberg, E., Gilligan, C., Bartley, E., McDermott, J., Brabazon, A.: GEVA: Grammatical Evolution in Java. SIGEVOlution ACM 3, 17–23 (2008)
Norman, P.: Genetic programming with context-sensitive grammars. Phd thesis, Saint Andrew’s University (2002)
Wong, M.L., Leung, K.S.: Data Mining Using Grammar-Based Genetic Programming and Applications. Kluwer Academic Publishers (2000)
Montana, D.J.: Strongly typed genetic programming. Evolutionary Computation Journal 3, 199–230 (1995)
McKay, R., Hoai, N.X., Whigham, P.A., Shan, Y., O’Neill, M.: Grammar-based Genetic Programming: A review. Genetic Programming and Evolvable Machines 11, 365–395 (2010)
McKay, R.I., Nguyen, X.H., Whigham, P.A., Shan, Y.: Grammars in Genetic Programming: A Brief Review. In: Progress in Intelligence Computation and Intelligence: Proceedings of the International Symposium on Intelligence, Computation and Applications, pp. 3–18 (2005)
Nohejl, A.: Grammar Based Genetic Programming. MSc Thesis, Charles University of Prague (2011)
Nohejl, A.: Grammatical Evolution. BSc Thesis, Charles University of Prague (2009)
Freitas, A.A.: Data mining and Knowledge Discovery with evolutionary algorithms. Springer (2002)
Bing, L.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer Edition (2011)
Pappa, L.G.: Automatically Evolving Rule Induction Algorithms using Grammar-based Genetic Programming. Phd Thesis, Kent University (2007)
Dempsey, I., O’Neill, M., Brabazon, A.: Foundations in Grammatical Evolution for Dynamic Environments. SCI, vol. 194. Springer, Heidelberg (2009)
UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
Oltean, M., Grosan, C.: A Comparison of Several Linear Genetic Programming Techniques. Complex Systems Journal 14, 285–313 (2004)
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Mazouni, R., Rahmoun, A. (2015). AGGE: A Novel Method to Automatically Generate Rule Induction Classifiers Using Grammatical Evolution. In: Camacho, D., Braubach, L., Venticinque, S., Badica, C. (eds) Intelligent Distributed Computing VIII. Studies in Computational Intelligence, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-319-10422-5_30
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DOI: https://doi.org/10.1007/978-3-319-10422-5_30
Publisher Name: Springer, Cham
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