Large scale instance selection by means of federal instance selection

Data and Knowledge Engineering

Volumn 75, Issue , 2012, Pages 58-77

  De Haro García, Aida ^a   García Pedrajas, Nicolás ^a   Del Castillo, Juan Antonio Romero ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Instance selection; Instance based learning; Parallel algorithms; Scalability; Very large problems

Indexed keywords

DIGITAL STORAGE; LEARNING SYSTEMS; PARALLEL ALGORITHMS; SCALABILITY;

EXECUTION TIME; FAST EXECUTION TIME; INSTANCE BASED LEARNING; INSTANCE SELECTION; LARGE DATASETS; LARGE PROBLEMS; SCALING PROBLEM; UCI MACHINE LEARNING REPOSITORY;

COMPUTATIONAL COMPLEXITY;

EID: 84861094585     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2012.03.002     Document Type: Article

References (32)

1. M. Craven, D. DiPasquoa, D. Freitagb, A. McCalluma, T. Mitchella, K. Nigama, and S. Slatterya Learning to construct knowledge bases from the world wide web Artificial Intelligence 118 1-2 2000 69 113
2. F.J. Provost, and V. Kolluri A survey of methods for scaling up inductive learning algorithms Data Mining and Knowledge Discovery 2 1999 131 169
3. N. García-Pedrajas, and A. de Haro-García Scaling up data mining algorithms: review and taxonomy Progress in Artificial Intelligence 1 2012 1 17
4. H. Liu, H. Motada, and L. Yu A selective sampling approach to active feature selection Artificial Intelligence 159 1-2 2004 49 74
5. C. García-Osorio, A. de Haro-García, and N. García-Pedrajas Democratic instance selection: a linear complexity instance selection algorithm based on classifier ensemble concepts Artificial Intelligence 174 2010 410 441
6. D. Asimov The grand tour: a tool for viewing multidimensional data SIAM Journal on Scientific and Statistical Computing 6 1 1985 128 143
7. J.R. Cano, F. Herrera, and M. Lozano Stratification for scaling up evolutionary prototype selection Pattern Recognition Letters 26 7 2005 953 963 (Pubitemid 40538755)
8. J. Derrac, S. García, and F. Herrera Stratified prototype selection based on a steady-state memetic algorithm: a study of scalability Memetic Computing 2 2010 183 189
9. D.R. Wilson, and T.R. Martinez Reduction techniques for instance-based learning algorithms Machine Learning 38 2000 257 286 (Pubitemid 30572450)
10. F. Angiulli Fast nearest neighbor condensation for large data sets classification IEEE Transactions on Knowledge and Data Engineering 19 2007 1450 1464 (Pubitemid 47573790)
11. S. García, J.R. Cano, and F. Herrera A memetic algorithm for evolutionary prototype selection: a scaling up approach Pattern Recognition 41 2008 2693 2709
12. J.R. Cano, F. Herrera, and M. Lozano Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability Data & Knowledge Engineering 60 1 2007 90 108 (Pubitemid 46053591)
13. N. García-Pedrajas Evolutionary computation for training set selection Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1 6 2011 512 523
14. J. Beringer, and E. Hüllermeier Online clustering of parallel data streams Data & Knowledge Engineering 58 2006 180 204 (Pubitemid 43795465)
15. M. Dash, S. Petrutiu, and P. Scheuermann pPOP: Fast yet accurate parallel hierarchical clustering using partitioning Data & Knowledge Engineering 61 2007 563 578 (Pubitemid 46578259)
16. N.J.E. Wijngaards, B.J. Overeinder, M. van Steen, and F.M.T. Brazier Supporting internet-scale multi-agent systems Data & Knowledge Engineering 41 2002 229 245 (Pubitemid 34449167)
17. I. Czarnowski Prototype selection algorithms for distributed learning Pattern Recognition 43 2010 2292 2300
18. P. Jedrzejowicz Social learning algorithm as a tool for solving some difficult scheduling problems Foundation of Computing and Decision Sciences 24 1999 51 66
19. N.O. Andrews, and E.A. Fox Clustering for Data Reduction: A Divide and Conquer Approach Tech. rep., Virginia Tech 2007
20. H. Brighton, and C. Mellish Advances in instance selection for instance-based learning algorithms Data Mining and Knowledge Discovery 6 2002 153 172 (Pubitemid 37113870)
21. G.W. Gates The reduced nearest neighbor rule IEEE Transactions on Information Theory 18 3 1972 431 433
22. R. Barandela, F.J. Ferri, and J.S. Sánchez Decision boundary preserving prototype selection for nearest neighbor classification International Journal of Pattern Recognition and Artificial Intelligence 19 6 2005 787 806 (Pubitemid 41374104)
23. J.R. Cano, F. Herrera, and M. Lozano Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study IEEE Transactions on Evolutionary Computation 7 6 2003 561 575
24. F. Wilcoxon Individual comparisons by ranking methods Biometrics 1 1945 80 83
25. J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 (Pubitemid 43022939)
26. A. Orriols-Puig, and E. Bernadó-Mansilla Evolutionary rule-based systems for imbalanced data sets Soft Computing 13 2009 213 225
27. N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer SMOTE: synthetic minority over-sampling technique Journal of Artificial Intelligence Research 16 2002 321 357 (Pubitemid 43057176)
28. S. García, and F. Herrera Evolutionary undersampling for classification with imbalanced datasets: proposals and taxonomy Evolutionary Computation 17 3 2009 275 306
29. N. García-Pedrajas Constructing ensembles of classifiers by means of weighted instance selection IEEE Transactions on Neural Networks 20 2 2008 258 277
30. Y. Sun, M.S. Kamel, A.K.C. Wong, and Y. Wang Cost-sensitive boosting for classification of imbalanced data Pattern Recognition 40 2007 3358 3378 (Pubitemid 47223287)
31. D. Lewis, and W. Gale Training text classifiers by uncertainty sampling Proceedings of the Seventeenth Annual International ACM SIGIR Conference on Research and Development in Information, New York, USA 1998 73 79
32. M. Kubat, R. Holte, and S. Matwin Machine learning for the detection of oil spills in satellite radar images Machine Learning 30 1998 195 215