Encoding and decoding the knowledge of association rules over SVM classification trees

Knowledge and Information Systems

Volumn 19, Issue 1, 2009, Pages 79-105

  Pang, Shaoning ^a   Kasabov, Nikola ^a  

^a AUCKLAND UNIVERSITY OF TECHNOLOGY   (New Zealand)

Author keywords

Association rule extraction; Class imbalance; Class overlap; Support vector machine; SVM aggregating intelligence; SVM classification tree; SVM ensemble

Indexed keywords

ASSOCIATION RULES; BENCHMARKING; CLASSIFICATION (OF INFORMATION); DATA MINING; DECISION TREES; DECODING; EXTRACTION; MATLAB; SIGNAL ENCODING; TREES (MATHEMATICS);

CLASS IMBALANCE; CLASS OVERLAP; SVM AGGREGATING INTELLIGENCE; SVM CLASSIFICATION TREE; SVM ENSEMBLES;

SUPPORT VECTOR MACHINES;

EID: 63649143946     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-008-0147-1     Document Type: Article

References (49)

1. Nunez H, Angulo C, Catala A (2002) Rule-extraction from support vector Machines. In: The European symposiumon aritificial neural networks, Burges, pp 107-112
2. Zhang Y, Su HY, Jia T, Chu J (2005) Rule extraction from trained support vector machines, PAKDD 2005, LANI3518. Springer, Heidelberg, pp 61-70
3. Wang L, Fu X (2005) Rule extraction from support vector machine. In: Data mining with computational intelligence, nced information and knowlegde processing. Springer, Berlin
4. Barakat N, Bradley AP (2006) Rule extraction from support vector machines: Measuring the explanation capability using the area under the ROC curve. In: The 18th international conference on pattern recognition (ICPR'06), August, 2006, Hong Kong
5. Fung G, Sandilya S, Rao B (2005) Rule extraction for linear support vector machines, KDD2005, August 21-24, 2005, Chicago
6. Fu X, Ong C, Keerthi S, Huang GG, Goh L (2004) Extracting the knowledge embedded in support vector machines. In: Proceedings of IEEE international joint conference on neural networks, vol 1, no 25-29 July 2004, pp 291-296
7. Vapnik V (1982) Estimation of dependences based on empirical data. Springer, Heidelberg
8. Vapnik V (1995) The nature of statistical learning theory. Spinger, Heidelberg
9. Cortes C, Vapnik V (1995) Support vector network. Mach Learning 20:273-297
10. Pang S, Ozawa S, Kasabov N (2004) One-pass incremental membership authentication by face classification. ICBA 2004, LNCS, vol 3072. Springer, Heidelberg, pp 155-161
11. Pang S, Kim D, Bang SY (2003) Membership authentication in the dynamic group by face classification using SVM ensemble. Patt Recogn Lett 24:215-225
12. Pang S (2005) SVM aggregation: SVM, SVM ensemble, SVM classification tree, IEEE SMC eNewsletter Dec. 2005. http://www.ieeesmc.org/Newsletter/ Dec2005/R11Pang.php
13. Pang S, Kim D, Bang SY (2005) Face membership authentication using svm classification tree generated by membership-based LLE data partition. IEEE Trans Neural Netw 16(2):436-446
14. Schölkopf JC, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (1999) Estimating the support of a high-dimensional distribution. Technical report, Microsoft Research, MSR-TR-99-87
15. Tax DMJ (2001) One-class classification, concept-learning in the absence of counter-examples. PhD Thesis
16. Tax DMJ, Duin RPW (2001) Combining one-class classifiers. LNCS 2096:299-308
17. Xu Y, Brereton RG (2005) Diagnostic pattern recognition on gene expression profile data by using one-class classifiers. J Chem Inf Model 45:1392-1401
18. Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo
19. Kim H-C, Pang S, Je H-M, Kim D, Yang Bang S (2003) Constructing support vector machine ensemble. Patt Recogn 36(12):2757-2767
20. Shipp MA, Ross KN et al (2002) Supplementary information for diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8(1):68-74
21. Golub TR (2004) Toward a functional taxonomy of cancer. Cancer Cell 6(2):107-8
22. Pomeroy S, Tamayo P et al (2002) Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415(6870):436-442
23. Alon U, Barkai N et al (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 8:6745-6750
24. Petricoin EF, Ardekani AM et al (2002) Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359:572-577
25. Van't Veer LJ et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530-536
26. Gordon GJ, Jensen R et al (2002) Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Res 62:4963-4967
27. Dudoit S, Yang YH, Callow MJ, Speed TP (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sin 12:111-139
28. Schuster A, Wolff R, Trock D (2005) A high-performance distributed algorithm for mining association rules. Knowl Inf Syst 7:458-475
29. Kam Ho T (1998) The random subspace method for constructing decision forests Tin Kam Ho. IEEE Trans Patt Anal Mach Intell 20(8):832-844
30. NeuCom - A Neuro-computing Decision Support Enviroment, Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, http://www.theneucom.com
31. Nez H, Angulo C, Catal A (2003) Hybrid Architecture based on support vector machines. In: Book Computational Methods in Neural Modeling Lecture Notes in Computer Science, vol 2686, pp 646-653
32. Zhou ZH, Jiang Y (2003) Medical diagnosis with C4.5 rule preceded by artificial neural netowrk ensemble. IEEE Trans Inf Technol Biomed 7(1):37-42
33. Chen Y, Wang JZ (2003) Support vector learning for fuzzy rule-based classification systems. IEEE Trans Fuzzy Syst 11(6):716-728
34. Nunez H, Angulo C, Catala A (2002) Support vector machines with symbolic interpretation. In: Proceedings of VII Brazilian symposium on neural networks, 11-14 Nov. 2002, pp 142-147
35. Duch W, Setiono R, Zurada JM (2004) Computational intelligence methods for rule-based data understanding. Proc IEEE 92(5):771-805
36. Pang S, Kim D, Bang SY (2001) Fraud detection using support vector machine ensemble. ICONIP2001, Shanghai, China
37. Terabe M, Washio T, Motoda H, Katai O, Sawaragi T (2002) Attribute generation based on association rules. Knowl Inf Syst 4:329-349
38. Barakat N, Diederich J (2004) Learning-based rule-extraction from support vector machines: Performance on benchmark data sets. In: Proceedings of conference on neuro-computing and evolving intelligence, Dec. 2004
39. Barakat N, Diederich J (2005) Eclectic rule-extraction from support vector machines. Int J Comput Intell 2(1):59-62
40. Barakat N, Bradley A (2007) Rule extraction from support vector machines: A sequential covering apporoach. IEEE Trans Knowl Data Eng 19(6):729-741
41. Provost F (2000) Machine Learning from Imbalanced Data Sets 101. Working Notes AAAI'00 workshop learning from imbalanced data sets, pp 1-3
42. Wu G, Chang E (2005) KBA: Kernel boundary alignment considering imbalance data distribution. IEEE Trans Knowl Data Eng 17(6):786-795
43. Wu G, Chang E (2003) Adaptive feature-space conformal transformation for imbalanced data learning. In: Proceedings of 20th internatuional conference on machine learning, pp 816-823
44. Lin Y, Lee Y, Wahba G (2002) Support vector machines for classification in nonstandard situations. Mach Learn 46:191-101
45. Veropoulos K, Campbell C, Cristianini N (1999) Controlling the sensitivity of support vector machine. In: Proceedings of international joint conference on artifical intelligence, pp 55-60
46. Estabrooks J, Japkowicz N (2004) A multiple resampling method for learning from imbalanced data sets. Comput Intell 20:18-36
47. Zhou ZH, Liu XY (2006) Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowl Data Eng 19(1):63-77
48. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res (JAIR) 16:321-357
49. Falco De, Della Cioppa A, Iazzetta A, Tarantino E (2005) An evolutionary approach for automatically extracting intelligible classification rules. Knowl Inf Syst 7:179-201