GP classification under imbalanced data sets: Active sub-sampling and AUC approximation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4971 LNCS, Issue , 2008, Pages 266-277

  Doucette, John ^a   Heywood, Malcolm I ^a  

^a DALHOUSIE UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COMPUTER GRAPHICS; COMPUTER PROGRAMMING; FUNCTION EVALUATION; GENETIC ALGORITHMS; GENETIC PROGRAMMING; PROBABILITY DISTRIBUTIONS; RISK ASSESSMENT; STATISTICAL METHODS;

ACTIVE LEARNING (AL); AREA UNDER THE CURVE (AUC); BALANCE (WEIGHTING); BINARY CLASSIFICATION; DATA SETS; EFFICIENT ESTIMATORS; EUROPEAN; FITNESS EVALUATION; FITNESS FUNCTIONS; HEIDELBERG (CO); IMBALANCED DATA SETS; MACHINE LEARNING LITERATURE; MANN WHITNEY; NAIVE BAYES (NB); PERFORMANCE METRIC; UNBALANCED DATA; UNIFORM PROBABILITY;

LEARNING SYSTEMS;

EID: 47249113963     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-78671-9_23     Document Type: Conference Paper

References (22)

1. Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145-1159 (1997)
2. Brameier, M., Banzhaf, W.: A comparison of linear Genetic Programming and Neural Networks in medical data mining. IEEE Transactions on Evolutionary Computation 5(1), 17-26 (2001)
3. Cartlidge, J., Bullock, S.: Learning lessons from the common cold: How reducing parasite virulence improves coevolutionary optimization. In: IEEE Congress on Evolutionary Computation, pp. 1420-1425 (2002)
4. de Jong, E.D.: A monotonic archive for pareto-coevolution. Evolutionary Computation 15(1), 61-94 (2007)
5. de Jong, E.D., Pollack, J.B.: Ideal evaluation from coevolution. Evolutionary Computation 12(2), 159-192 (2004)
6. Eggermont, J., Eiben, A.E., van Hermert, J.I.: Adapting the fitness function in GP for data mining. In: Langdon, W.B., Fogarty, T.C., Nordin, P., Poli, R. (eds.) EuroGP 1999. LNCS, vol. 1598, pp. 193-202. Springer, Heidelberg (1999)
7. Ficici, S.G., Pollock, J.B.: Pareto optimality in coevolutionary learning. In: European Conference on Artificial Life, pp. 316-325 (2001)
8. Gathercole, C., Ross, P.: Dynamic training subset selection for supervised learning in genetic programming. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 312-321. Springer, Heidelberg (1994)
9. Hand, D.J.: Construction and Assessment of Classification Rules. John Wiley, Chichester (1997)
10. Hillis, W.D.: Co-evolving parasites improve simulated evolution as an optimization procedure. In: Artificial Life II. Santa Fe Institute Studies in the Sciences of Complexity, vol. X, pp. 313-324 (1990)
11. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
12. Langdon, W.B., Buxton, B.F.: Evolving Receiver Operating Characteristics for Data Fusion. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 87-96. Springer, Heidelberg (2001)
13. Lemczyk, M., Heywood, M.I.: Training binary GP classifiers efficiently: a Pareto-coevolutionary approach. In: Ebner, M., O'Neill, M., Eḱart, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 229-240. Springer, Heidelberg (2007)
14. Lichodzijewski, P., Heywood, M.I.: Pareto-coevolutionary Genetic Programming for problem decomposition in multi-class classification. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), vol. 1, pp. 464-471 (2007)
15. McIntyre, A.R., Heywood, M.I.: Toward co-evolutionary training of a multi-class classifier. In: Proceedings of the Congress on Evolutionary Computation (CEC), vol. 3, pp. 2130-2137 (2005)
16. Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/∼mlearn/ mlrepository.html
17. Noble, J., Watson, R.A.: Pareto coevolution: Using performance against coevolved opponents in a game as dimensions for Pareto selection. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 493-500 (2001)
18. Patterson, G., Zhang, M.: Fitness functions in Genetic Programming for classification with unbalanced data. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 464-471. Springer, Heidelberg (2007)
19. Song, D., Heywood, M.I., Zincir-Heywood, A.N.: Training Genetic Programming on half a million patterns: An example from anomaly detection. IEEE Transactions on Evolutionary Computation 9(3), 225-239 (2005)
20. Weiss, G.M., Provost, R.: Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research 19, 315-354 (2003)
21. Yan, L., Dodier, R., Mozer, M.C., Wolniewicz, R.: Optimizing classifier performance via the Wilcoxon-Mann-Whitney statistic. In: Proceedings of the International Conference on Machine Learning, pp. 848-855 (2003)
22. Zoungker, D., Punch, B.: lilgp genetic programming system. version 1.1 (1998), http://garage.cse.msu.edu/