LODE: A distance-based classifier built on ensembles of positive and negative observations

Pattern Recognition

Volumn 45, Issue 4, 2012, Pages 1409-1425

  Meo, Rosa ^a   Bachar, Dipankar ^a,b   Ienco, Dino ^a,c  

^a UNIVERSITY OF TURIN   (Italy)

^b LABORATOIRE D OCÉANOGRAPHIE DE VILLEFRANCHE   (France)

^c UMR TETIS   (France)

Author keywords

Associative classifier; Concept learning; Data mining; Supervised learning

Indexed keywords

ASSOCIATIVE CLASSIFIERS; CLASS MODELS; CONCEPT LEARNING; DISTANCE COMPUTATION; DISTANCE-BASED; EMPIRICAL EXPERIMENTS; GLOBAL MODELS; ITEM SETS; LOCAL PATTERNS; OVERFITTING; PREDICTION ACCURACY; REPRESENTATION SPACE; STATE OF THE ART; TARGET CLASS; TEST EXAMPLES; TRAINING DATA;

DATA MINING; FORECASTING; SUPERVISED LEARNING; VECTOR SPACES; VECTORS;

FEATURE EXTRACTION;

EID: 83655184747     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2011.10.015     Document Type: Article

References (45)

1. B. Liu, W. Hsu, and Y. Ma Integrating classification and association rule mining Proceedings of KDD 1998 80 86
2. W. Li, J. Han, and J. Pei CMAR: accurate and efficient classification based on multiple class-association rules ICDM, International Conference on Data Mining 2001 369 376
3. H. Cheng, X. Yan, J. Han, and C.-W. Hsu Discriminative frequent pattern analysis for effective classification ICDE 2007 IEEE Computer Society Los Alamitos, CA, USA 716 725 (Pubitemid 47422075)
4. W. Fan, K. Zhang, H. Cheng, J. Gao, X. Yan, J. Han, P. Yu, and O. Verscheure Direct mining of discriminative and essential frequent patterns via model-based search tree Proceedings of KDD '08 2008 230 238
5. X. Yan, H. Cheng, J. Han, and P.S. Yu Mining significant graph patterns by leap search Proceedings of SIGMOD '08 2008 433 444
6. G. Dong, X. Zhang, L. Wong, and J. Li CAEP: classification by aggregating emerging patterns Discovery Science 1999 Springer Berlin, Heidelberg 737
7. H. Fan, and K. Ramamohanarao Fast discovery and the generalization of strong jumping emerging patterns for building compact and accurate classifiers IEEE Transactions on Knowledge and Data Engineering 18 6 2006 721 737
8. J. Li, G. Dong, K. Ramamohanarao, and L. Wong DeEPs: a new instance-based lazy discovery and classification system Machine Learning 54 2 2004 99 124
9. W. Cohen Fast effective rule induction Proceedings of International Conference on Machine Learning 1995 115 123
10. D. Aha, and D. Kibler Instance-based learning algorithms Machine Learning 6 1991 37 66 (Pubitemid 21727227)
11. K. Wang, S. Zhou, and Y. He Growing decision trees on support-less association rules Proceedings of KDD'00 2000 ACM New York, NY, USA 265 269
12. L. Breiman Random forests Machine Learning 45 1 2001 5 32 (Pubitemid 32933532)
13. Y. Freund, and R.E. Schapire Experiments with a new boosting algorithm Proceedings of ICML 1996 148 156
14. J. Platt Fast training of support vector machines using sequential minimal optimization Advances in Kernel Methods: Support Vector Learning 1999 MIT Press 185 208
15. A. Knobbe, B. Cremilleux, J. Furnkranz, and M. Scholz From local patterns to global models: the LeGo approach to data mining From Local Patterns to Global Models: Proceedings of ECML-PKDD 2008 Workshop 2008 1 16
16. J.-N. Sulzmann, and J. Furnkranz A comparison of techniques for selecting and combining class association rules From Local Patterns to Global Models: Proceedings of ECML-PKDD 2008 Workshop 2008 154 168
17. R.S. Michalski, L. Mozetic, L. Hong, and N. Lavrac The multi-purpose incremental learning system AQl5 and its testing application to three medical domains Proceedings of AAAI-86 1986 1041 1045
18. R. Agrawal, T. Imielinski, and A. Swami Mining association rules between sets of items in large databases Proceedings of ACM SIGMOD 1993
19. R. Meo Theory of dependence values ACM TODS 2000 45 (3)
20. D. Bachar, and R. Meo A novel distance-based classifier built on pattern ranking Proceedings of ACM SAC 2009 1427 1432
21. T.M. Cover, and J.A. Thomas Elements of Information Theory 1991 Wiley
22. J.H. Friedman, and B.E. Popescu Predictive learning via rule ensembles The Annals of Applied Statistics 2 3 2008 916 954
23. Y. Sun, Y. Wang, and A. Wong Boosting an associative classifier IEEE Transactions on Knowledge and Data Engineering 18 7 2006 988 992 (Pubitemid 44143906)
24. Y. Wang, and A. Wong High order pattern discovery from discrete valued data IEEE Transactions on Knowledge and Data Engineering 9 6 1997 877 893
25. Y. Wang, and A. Wong From association to classification: inference using weight of evidence IEEE Transactions on Knowledge and Data Engineering 15 3 2003 764 767
26. E.M. Nguifo, and P. Njiwoua IGLUE: a lattice-based constructive induction system International Journal of Intelligent Data Analysis (IDA) 4 2001 1 49
27. E. Baralis, S. Chiusano, and P. Garza A lazy approach to associative classification IEEE Transactions on Knowledge and Data Engineering 20 2 2008 156 171
28. T.M. Cover, and P.E. Hart Nearest neighbor pattern classification IEEE Transactions on Information Theory 13 1967 21 27
29. D.R. Wilson, and T.R. Martinez Reduction techniques for instance-based learning algorithms Machine Learning 38 3 2000 257 286 (Pubitemid 30572450)
30. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann Publishers
31. G.H. John, and P. Langley Estimating continuous distributions in Bayesian classifiers Proceedings of 11th Conference on Uncertainty in Artificial Intelligence 1995 Morgan Kaufmann 338 345
32. V. Vapnik Statistical Learning Theory 1998 John Wiley and Sons New York
33. B. Pfahringer, G. Holmes, and C. Wang Millions of random rules Proceedings of Workshop on Advances in Inductive Rule Learning, at ECML 2004 Springer-Verlag
34. T. Uno, T. Asai, Y. Uchida, and H. Arimura LCM ver. 2: efficient mining algorithms for frequent/closed/maximal itemsets ICDM FIMI Workshop 2004
35. R. Meo Maximum independence and mutual information IEEE Transactions on Information Theory 48 1 2002
36. U.M. Fayyad, and K.B. Irani Multi-interval discretization of continuous valued attributes for classification learning Proceedings of 13th IJCAI 1993 1022 1027
37. A. Knobbe, and E. Ho Maximally informative k-itemsets and their efficient discovery Proceedings of KDD 2006 244 253
38. A. Knobbe, and E. Ho Pattern teams Proceedings of European Conference on Principles and Practice of Knowledge Discovery in Databases 2006 577 584 (Pubitemid 44617760)
39. D. Gamberger, and N. Lavrac Expert-guided subgroup discovery: methodology and application Journal of Artificial Intelligence Research 17 1 2002 501 527
40. N. Lavrac, P. Flach, B. Kavsek, and L. Todorovski Adapting classification rule induction to subgroup discovery Proceedings of ICDM '02 2002
41. V. Jovanoski, and N. Lavrac Classification rule learning with APRIORI-C Proceedings of EPIA '01 2001 Springer-Verlag 44 51
42. T. Steinbach, and V. Kumar Introduction to Data Mining 2006 Pearson Education
43. S. Keerthi, S. Shevade, C. Bhattacharyya, and K. Murthy Improvements to Platts SMO algorithm for SVM classifier design Neural Computation 13 3 2001 637 649 (Pubitemid 33595014)
44. R. Kohavi The power of decision tables N. Lavrac, S. Wrobel, Proceedings of ECML, Lecture Notes in Artificial Intelligence vol. 914 1995 Springer Verlag Berlin, Heidelberg, New York 174 189
45. J. Demsar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 (Pubitemid 43022939)