Merging local patterns using an evolutionary approach

Knowledge and Information Systems

Volumn 29, Issue 1, 2011, Pages 1-24

  Gaya, María C ^a   Giráldez, J Ignacio ^a  

^a UNIVERSIDAD EUROPEA DE MADRID   (Spain)

Author keywords

Distributed data mining; Genetic algorithms; Multi agent systems; Multi database mining

Indexed keywords

EID: 80053318379     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-010-0332-x     Document Type: Article

References (44)

1. ECML, PKDD (2006) ECML PKDD 2006 discovery challenge. Downloaded in 2010, from http://www. ecmlpkdd2006. org/challenge. html.
2. Asuncion A, Newman D (2007) UCI machine learning repository. Downloaded in 2008, from http://archive. ics. uci. edu/ml/.
3. Barandela R, Valdovinos RM, Sánchez JS (2003) New applications of ensembles of classifiers. Pattern Anal Appl 6(3): 245-256.
4. Bauer E, Kohavi R (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. J Mach Learn 36(1-2): 105-139.
5. Bellifemine F, Caire G, Greenwood D (2007) Developing multi-agent systems with JADE. Wiley, NewYork.
6. Breiman L (1996) Bagging predictors. Mach Learn 24(2): 123-140.
7. Breiman L (2001) Random forest. Mach Learn 45(1): 5-32.
8. Dietterich TG (2000) Ensemble methods in machine learning. Multiple Classifier Syst 1857: 1-15.
9. Dzeroski S, Zenko B (2004) Is combining classifiers with stacking better than selecting the best one?. Mach Learn 54(3): 255-273.
10. Freitas A (2007) A review of evolutionary algorithms for data mining. In: Maimon O, Rokach L (eds) Soft computing for knowledge discovery and data mining. Springer, Berlin, pp 61-93.
11. Freund Y (1998) An introduction to boosting based classification. AT&T conference on quantitative analysis.
12. Gama J, Brazdil P (2000) Cascade generalization. Mach Learn 41(3): 315-343.
13. García-Pedrajas N, García-Osorio C, García-Osorio C (2007) Nonlinear boosting projections for ensemble construction. J Mach Learn Res 8: 1-33.
14. Giráldez JI (1999) Modelo de toma de decisiones y aprendizaje en sistemas multi-agente. Tesis para el grado de doctor en Informática, Universidad Politécnica de Madrid.
15. Grossman RL, Turinsky AL (2000) A framework for finding distributed data mining strategies that are intermediate between centralized strategies and in-place strategies. KDD workshop on distributed data mining, knowledge and information systems.
16. Gaya MC, Giráldez JI (2008) Experiments in multi agent learning. In: Hybrid artificial intelligence systems, Third International Workshop, HAIS 2008. Springer, Burgos Spain, pp 78-85.
17. Gaya MC, Giráldez JI (2008) Techniques for distributed theory synthesis in multiagent systems. In: International symposium on distributed computing and artificial intelligence, DCAI 2008 advances in soft computing. Springer, Salamanca Spain, pp 395-402.
18. Gaya MC, Giráldez JI, Cortizo JC (2007) Uso de algoritmos evolutivos para la fusión de teorías en minería de datos distribuida. CAEPIA'07, Salamanca.
19. Guo H, Viktor HL (2008) Multirelational classification: a multiple view approach. Knowledge Inf Syst 17(3): 287-312.
20. Hernández J, Ramírez MJ, Ferri C (2004) Introducción a la Minería de Datos". Pearson Prentice-Hall, Madrid.
21. Ho TK (1995) Random decision forests. In: 3rd International conference on document analysis and recognition. Montreal, Canada, pp 278-282.
22. Ho T (1998) Nearest neighbors in random subspaces. Lecture notes in computer science: advances in pattern recognition, pp 640-648.
23. Hongjun HL, Liu H, Lu H, Yao J (2001) Towards multidatabase mining: identifying relevant databases. IEEE Trans Knowl Data Eng 13(4): 541-553.
24. Kim Y, Street WN, Menczer F (2006) Optimal ensemble construction via meta-evolutionary ensembles. Expert Syst Appl 30(4): 705-714.
25. Ko AH-R, Sabourin R, Souz AD (2006) Evolving ensemble of classifiers in random subspace. In: Annual conference on genetic and evolutionary computation GECCO '06.
26. Koza J, Keane M, Streeter M, Mydlowec W, Yu J, Lanza G (1992) Genetic programming IV: routine human-competitive machine intelligence. Springer, Berlin.
27. Kuncheva LI (2001) Combining classifiers: soft computing solutions. In: Pal SK, Pal A (eds) Pattern recognition: from classical to modern approaches. World Scientific Publishing Co, Singapore, pp 427-452.
28. Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles. Mach Learn 51(2): 181-207.
29. Langdom W, Buxtom B (2001) Genetic programing for combining classifiers. Genetic and evolutionary computation GECCO. Morgan Kaufmann, UK, pp 66-73.
30. Luo H, Fan J, Lin X, Zhou A, Bertino E (2009) A distributed approach to enabling privacy-preserving model-based classifier training. Knowledge Inf Syst 20(2): 157-185.
31. Merz CJ (1999) Using correspondence analysis to combine classifiers. Mach Learn 36(1-2): 33-58.
32. Mitchel TM (1997) Machine learning. McGraw-Hill, NY.
33. Quinlan R (1994) C4. 5: Programs for machine learning. Mach Learn 16(3): 235-240.
34. Ramkumar T, Srinivasan R (2008) Modified algorithms for synthesizing high-frequency rules from different data sources. Knowledge Inf Syst 17(3): 313-334.
35. Schapire RE (2002) Advances in boosting. In: Eighteenth conference on uncertainty in artificial intelligence.
36. Stolfo S, Prodromidis A, Tselepis S, Lee W, Fan DW, Chan PK (1997) JAM: Java agents for meta-learning over distributed databases. In: Third international conference in knowledge discovery and data mining. Newport Beach, California, pp 74-81.
37. Ting KM, Witten IH (1999) Issues in stacked generalization. J Artif Intell Res (JAIR) 10: 271-289.
38. Todorovski L, Dzeroski S (2003) Combining classifiers with meta decision trees. Mach Learn 50(3): 223-249.
39. Webb GI (2000) MultiBoosting: a technique for combining boosting and wagging. Mach Learn 40(2): 159-196.
40. Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Los Altos.
41. Wolpert DH (1992) Stacked generalization. Neural Netw 5: 241-259.
42. Wu X, Zhang S (2003) Synthesizing high-frequency rules from different data sources. IEEE Trans Knowledge Data Eng 15(2): 353-367.
43. Zhang S, Zaki MJ (2006) Mining multiple data sources: local pattern analysis. Data Mining Knowledge Discovery 12(2-3): 121-125.
44. Zhang S, Wu X, Zhang C (2003) Multi-database mining. IEEE Comput Intell Bull 2: 5-13.