Abstract
Existing learning and search algorithms can suffer a learning bias when dealing with unbalanced data sets. This paper proposes a Multi-Objective Genetic Programming (MOGP) approach to evolve a Pareto front of classifiers along the optimal trade-off surface representing minority and majority class accuracy for binary class imbalance problems. A major advantage of the MOGP approach is that by explicitly incorporating the learning bias into the search algorithm, a good set of well-performing classifiers can be evolved in a single experiment while canonical (single-solution) Genetic Programming (GP) requires some objective preference be a priori built into a fitness function. Our results show that a diverse set of solutions was found along the Pareto front which performed as well or better than canonical GP on four class imbalance problems.
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References
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Weiss, G.M., Provost, F.: Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research 19, 315–354 (2003)
Chawla, N.V., Japkowicz, N., Kolcz, A.: Editorial: Special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter 6, 1–6 (2004)
Bhowan, U., Johnston, M., Zhang, M.: Differentiating between individual class performance in genetic programming fitness for classification with unbalanced data. In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation (2009)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Coello, C., Lamont, G., Veldhuizen, D.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, US (2007)
Jong, E.D., Pollack, J.B.: Multi-objective methods for tree size control. Genetic Programming and Evolvable Machines 4(3), 211–233 (2003)
Parrot, D., Li, X., Ciesielski, V.: Multi-objective techniques in genetic programming for evolving classifiers. In: Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005), September 2005, pp. 1141–1148 (2005)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2000)
Bot, M.C.J., Boelelaan, D., Langdon, W.B.: Improving induction of linear classification trees with genetic programming. In: Genetic and Evolutionary Computation Conference, pp. 403–410. Morgan Kaufmann, San Francisco (2000)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30, 1145–1159 (1997)
Blake, C., Merz, C.: UCI repository of machine learning databases (1998), http://archive.ics.uci.edu/ml
Sung, K.-K.: Learning and Example Selection for Object and Pattern Recognition. PhD thesis, AI Laboratory and Center for Biological and Computational Learning, MIT (1996)
Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multiobjective optimization. Evolutionary Compututation 10(3), 263–282 (2002)
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Bhowan, U., Zhang, M., Johnston, M. (2009). Multi-Objective Genetic Programming for Classification with Unbalanced Data. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_38
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DOI: https://doi.org/10.1007/978-3-642-10439-8_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10438-1
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