KEEL: A software tool to assess evolutionary algorithms for data mining problems

Soft Computing

Volumn 13, Issue 3, 2009, Pages 307-318

  Alcala Fdez J ^a   Sánchez, Luciano ^b   García, Salva ^a   del Jesus, M J ^c   Ventura, S ^d   Garrell, Josep M ^e   Otero, J ^b   Romero, C ^d   Bacardit, J ^f   Rivas, V M ^c   Fernandez J C ^d   Herrera, F ^a  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF OVIEDO   (Spain)

^c UNIVERSITY OF JAÉN   (Spain)

^d UNIVERSITY OF CÓRDOBA   (Spain)

^e UNIVERSITAT RAMON LLULL   (Spain)

^f UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Computer based education; Data mining; Evolutionary computation; Experimental design; Graphical programming; Java; Knowledge extraction; Machine learning

Indexed keywords

EID: 55549103698     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-008-0323-y     Document Type: Article

References (58)

1. Alcalá R, Alcala-Fdez J, Casillas J, Cordón O, Herrera F (2006) Hybrid learning models to get the interpretabilityaccuracy trade-off in fuzzy modeling. Soft Comput 10(9):717-734
2. Batista GE, Monard MC (2003) An analysis of four missing data treatment methods for supervised learning. Appl Artif Intell 17:519-533
3. Bernadó-Mansilla E, Ho TK (2005) Domain of competence of XCS classifier system in complexity measurement space. IEEE Trans Evol Comput 9(1):82-104
4. Berthold MR, Cebron N, Dill F, Di Fatta G, Gabriel TR, Georg F, Meinl T, Ohl P (2006) KNIME: The Konstanz Information Miner, In: Proceedings of the 4th annual industrial simulation conference, Workshop on multi-agent systems and simulations, Palermo
5. Cano JR, Herrera F, Lozano M (2003) Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study. IEEE Trans Evol Comput 7(6):561-575
6. Cordón O, del Jesus MJ, Herrera F, Lozano M (1999) MOGUL: A methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach. Int J Intell Syst 14(9):1123-1153
7. Cordón O, Herrera F, Sánchez L (1999) Solving electrical distribution problems using hybrid evolutionary data analysis techniques. Appl Intell 10:5-24
8. Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, Singapore, p 488
9. Chuang AS (2000) An extendible genetic algorithm framework for problem solving in a common environment. IEEE Trans Power Syst 15(1):269-275
10. del Jesus MJ, Hoffmann F, Navascues LJ, Sánchez L (2004) Induction of Fuzzy-Rule-Based Classifiers with Evolutionary Boosting Algorithms. IEEE Trans Fuzzy Syst 12(3):296-308
11. Demšar J, Zupan B Orange: From experimental machine learning to interactive data mining, White Paper (http://www.ailab.si/orange). Faculty of Computer and Information Science, University of Ljubljana
12. Dietterich TG (1998) Approximate Statistica Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation 10(7):1895-1924
13. Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin, p 299
14. Freitas AA (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer, Berlin, p 264
15. Gagné C, Parizeau M (2006) Genericity in evolutionary computation sofyware tools: Principles and case-study Int J Artif Intell Tools 15(2):173-194
16. Ghosh A, Jain LC (2005) Evolutionary Computation in Data Mining. Springer, New York, pp 264
17. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, New York, pp 372
18. Grefenstette JJ (1993) Genetic Algorithms for Machine Learning. Kluwer, Norwell, p 176
19. Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, London, p 228
20. Keijzer M, Merelo JJ, Romero G, Schoenauer M (2001) Evolving objects: A general purpose evolutionary computation library. In: Collet P, Fonlupt C, Hao JK, Lutton E, Schoenauer M (eds) Artificial evolution: Selected papers from the 5th european conference on artificial evolution, London, UK, pp 231-244
21. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence 2(12):1137-1143
22. Krasnogor N, Smith J (2000) MAFRA: A Java memetic algorithms framework. In: Proceedings of the Genetic and Evolutionary Computation Workshops. Las Vegas, Nevada, USA, pp 125-131
23. Llorà X (2006) E2K: Evolution to knowledge. SIGEVOlution 1(3):10-16
24. Llorà X, Garrell JM (2003) Prototype induction and attribute selection via evolutionary algorithms. Int Data Anal 7(3):193-208
25. Liu H, Hussain F, Lim C, Dash M (2002) Discretization: An enabling technique. Data Min Knowl Discov 6(4):393-423
26. Luke S, Panait L, Balan G, Paus S, Skolicki Z, Bassett J, Hubley R, Chircop A (2007) ECJ: A Java based evolutionary computation research system. http://cs.gmu.edu/~eclab/projects/ecj
27. Martínez-Estudillo A, Martínez-Estudillo F, Hervás-Martínez C, García-Pedrajas N (2006) Evolutionary product unit based neural networks for regression. Neural Netw 19:477-486
28. Meyer M, Hufschlag K (2006) A generic approach to an object-oriented learning classifier system library. Journal of Artificial Societies and Social Simulation 9:3 http://jasss.soc.surrey.ac.uk/9/3/9.html
29. Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T (2006) YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1-6
30. Morik K, Scholz M (2004) The MiningMart Approach to Knowledge Discovery in Databases. In: Zhong N, Liu J (eds) Intelligent Technologies for Information Analysis. Springer, Heidelberg, pp 47-65
31. Mucientes M, Moreno DL, Bugarín A, Barro S (2006) Evolutionary learning of a fuzzy controller for wallfollowing behavior in mobile robotics. Soft Comput 10(10):881-889
32. Oh IS, Lee JS, Moon BR (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26(11):1424-1437
33. Ortega M, Bravo J (2000) Computers and education in the 21st century. Kluwer, Norwell, p 266
34. Otero J, Sánchez L (2006) Induction of descriptive fuzzy classifiers with the Logitboost algorithm. Soft Comput 10(9):825-835
35. Pal SK, Wang PP (1996) Genetic algorithms for pattern recognition. CRC Press, Boca Raton, p 336
36. Punch B, Zongker D (1998) lib-gp 1.1 beta. http://garage.cse.msu.edu/ software/lil-gp
37. Pyle D (1999) Data preparation for data mining. Morgan Kaufmann, San Mateo, p 540
38. Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo, p 316
39. R Development Core Team (2005) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria http://www.R-project.org
40. Rakotomalala R (2005) TANAGRA: Un logiciel gratuit pour l'enseignement et la recherche. In: Proceedings of the 5th Journées d'Extraction et Gestion des Connaissances 2:697-702
41. Rivera AJ, Rojas I, Ortega J, del Jesus MJ (2007) A new hybrid methodology for cooperative-coevolutionary optimization of radial basis function networks. Soft Comput 11(7):655-668
42. Rodríguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: A new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619-1630
43. Romero C, Ventura S, Bra P (2004) Knowledge discovery with genetic programming for providing feedback to courseware author, user modeling and user-adapted interaction. J Personal Res 14(5):425-465
44. Rummler A (2007) Evolvica: A Java framework for evolutionary algorithms. http://www.evolvica.org
45. Rushing J, Ramachandran R, Nair U, Graves S, Welch R, Lin H (2005) ADaM: a data mining toolkit for scientists and engineers. Comput Geosci 31(5):607-618
46. Sonnenburg S, Braun ML, Ong ChS, Bengio S, Bottou L, Holmes G, LeCun Y, Müller K-R, Pereira F, Rasmussen CE, Rätsch G, Schölkopf B, Smola A, Vincent P, Weston J, Williamson RC (2007) The need for open source software in machine learning. J Mach Learn Res 8:2443-2466
47. Stejić Z, Takama Y, Hirota K (2007) Variants of evolutionary learning for interactive image retrieval. Soft Comput 11(7):669-678
48. Tan JC, Lee TH, Khoo D, Khor EF (2001) A multiobjective evolutionary algorithm toolbox for computer-aided multiobjective optimization. IEEE Trans Syst Man Cybern B Cybern 31(4):537-556
49. Tan JC, Tay A, Cai J (2003) Design and implementation of a distributed evolutionary computing software. IEEE Trans Syst Man Cybern B Cybern 33(3):325-338
50. Tan PN, Steinbach M, Kumar V (2006) Introduction to Data Mining. Addison-Wesley, Reading, p 769
51. Ventura S, Romero C, Zafra A, Delgado JA, Hervás C (2008) JCLEC: a java framework for evolutionary computation. Soft Comput 12(4):381-392
52. Wang LX, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414-1427
53. Wang X, Nauck DD, Spott M, Kruse R (2007) Intelligent data analysis with fuzzy decision trees. Soft Comput 11(5):439-457
54. Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2):149-175
55. Wilson DR, Martinez TR (2000) Reduction techniques for instance-based learning algorithms. Mach Learn 38:257-268
56. Witten IH, Frank E (2005) Data mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco, p 525. http:// www.cs.waikato.ac.nz/ml/weka/index.html
57. Wong ML, Leung KS (2000) Data mining using grammar based genetic programming and applications. Kluwer, Norwell, p 232
58. Zhang S, Zhang C, Yang Q (2003) Data preparation for data mining. Appl Artif Intell 17:375-381