Detection of CAN by ensemble classifiers based on ripple down rules

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7457 LNAI, Issue , 2012, Pages 147-159

  Kelarev, Andrei ^a   Dazeley, Richard ^a   Stranieri, Andrew ^a   Yearwood, John ^a   Jelinek, Herbert ^b  

^a Centre for Informatics and Applied Optimization   (Australia)

^b CHARLES STURT UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; INTELLIGENT SYSTEMS; KNOWLEDGE ACQUISITION;

CHARLES STURT UNIVERSITIES; CLASSIFICATION MODELS; CONSENSUS FUNCTIONS; EMPIRICAL INVESTIGATION; ENSEMBLE CLASSIFIERS; MEDICAL PRACTITIONER; PERFORMANCE PARAMETERS; RESEARCH INITIATIVES;

KNOWLEDGE MANAGEMENT;

EID: 84962913495     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-32541-0_13     Document Type: Conference Paper

References (48)

1. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36, 105–139 (1999)
2. Bindoff, I., Kang, B.H.: Simulated Assessment of Ripple Round Rules. In: Kang, B.-H., Richards, D. (eds.) PKAW 2010. LNCS (LNAI), vol. 6232, pp. 180–194. Springer, Heidelberg (2010)
3. Bindoff, I., Kang, B.H.: Applying Multiple Classification Ripple Round Rules to a Complex Configuration Task. In: Wang, D., Reynolds, M. (eds.) AI 2011. LNCS (LNAI), vol. 7106, pp. 481–490. Springer, Heidelberg (2011)
4. Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
5. Compton, P., Jansen, R.: Knowledge in Context: a strategy for expert system maintenance. In: Second Australian Joint Artificial Intelligence Conference, AI 1988, pp. 292–306 (1988)
6. Compton, P., Jansen, R.: A philosophical basis for knowledge acquisition. Knowledge Acquisition 2, 241–258 (1990)
7. Compton, P., Peters, L., Edwards, G., Lavers, T.: Experience with Ripple-Down Rules. Knowledge-Based Systems 19(5), 356–362 (2006)
8. Cornforth, D., Jelinek, H.: Automated classification reveals morphological factors associated with dementia. Applied Soft Computing 8, 182–190 (2007)
9. Dazeley, R., Kang, B.: Generalising Symbolic Knowledge in Online Classification and Prediction. In: Richards, D., Kang, B.-H. (eds.) PKAW 2008. LNCS (LNAI), vol. 5465, pp. 91–108. Springer, Heidelberg (2009)
10. Dazeley, R., Park, S., Kang, B.: Online knowledge validation with prudence analysis in a document management application. Expert Systems with Applications 38, 10959–10965 (2011)
11. Dazeley, R., Warner, P., Johnson, S., Vamplew, P.: The Ballarat Incremental Knowledge Engine. In: Kang, B.-H., Richards, D. (eds.) PKAW 2010. LNCS (LNAI), vol. 6232, pp. 195–207. Springer, Heidelberg (2010)
12. Dazeley, R., Yearwood, J., Kang, B., Kelarev, A.: Consensus Clustering and Supervised Classification for Profiling Phishing Emails in Internet Commerce Security. In: Kang, B.-H., Richards, D. (eds.) PKAW 2010. LNCS (LNAI), vol. 6232, pp. 235–246. Springer, Heidelberg (2010)
13. Ewing, D., Campbell, J., Clarke, B.: The natural history of diabetic autonomic neuropathy. Q. J. Med. 49, 95–100 (1980)
14. Ewing, D., Martyn, C., Young, R., Clarke, B.: The value of cardiovascular autonomic function tests: 10 years experience in diabetes. Diabetes Care 8, 491–498 (1985)
15. Fern, X., Brodley, C.: Solving cluster ensemble problems by bipartite graph partitioning. In: 21st International Conference on Machine Learning, ICML 2004, vol. 69, pp. 36–43. ACM, New York (2004)
16. Finlayson, A., Compton, P.: Incremental Knowledge Acquisition Using Generalised RDR for Soccer Simulation. In: Kang, B.-H., Richards, D. (eds.) PKAW 2010. LNCS (LNAI), vol. 6232, pp. 135–149. Springer, Heidelberg (2010)
17. Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Proc. 13th Internat. Conf. Machine Learning, pp. 148–156 (1996)
18. Gaines, B., Compton, P.: Induction of Ripple-Down Rules applied to modeling large databases. J. Intell. Inf. Syst. 5(3), 211–228 (1995)
19. Ho, V., Compton, P., Benatallah, B., Vayssière, J., Menzel, L., Vogler, H.: An incremental knowledge acquisition method for improving duplicate invoices detection. In: Proceedings of the 25th International Conference on Data Engineering, ICDE 2009, pp. 1415–1418 (2009)
20. Huda, S., Jelinek, H., Ray, B., Stranieri, A., Yearwood, J.: Exploring novel features and decision rules to identify cardiovascular autonomic neuropathy using a hybrid of wrapper-filter based feature selection. In: Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2010, pp. 297–302 (2010)
21. Jelinek, H., Khandoker, A., Palaniswami, M., McDonald, S.: Heart rate variability and QT dispersion in a cohort of diabetes patients. Computing in Cardiology 37, 613–616 (2010)
22. Jelinek, H., Rocha, A., Carvalho, T., Goldenstein, S., Wainer, J.: Machine learning and pattern classification in identification of indigenous retinal pathology. In: Proceedings IEEE Conference Eng. Med. Biol. Soc., pp. 5951–5954 (2011)
23. Kang, B., Kelarev, A., Sale, A., Williams, R.: A New Model for Classifying DNA Code Inspired by Neural Networks and FSA. In: Hoffmann, A., Kang, B.-H., Richards, D., Tsumoto, S. (eds.) PKAW 2006. LNCS (LNAI), vol. 4303, pp. 187– 198. Springer, Heidelberg (2006)
24. Kang, B., Yoshida, K., Motoda, H., Compton, P.: A help desk system with intelligent interface. Applied Artificial Intelligence 11(7-8), 611–631 (1997)
25. Kang, B., Compton, P.: Multiple Classification Ripple Down Rules. In: Third Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop (1994)
26. Kang, B., Gambetta, W., Compton, P.: Verification and validation with ripple-down rules. International Journal of Human-Computer Studies 44(2), 257–269 (1996)
27. Karypis, G., Kumar, V.: METIS: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices. Technical report, University of Minnesota, Department of Computer Science and Engineering, Army HPC Research Centre, Minneapolis (1998)
28. Kelarev, A., Kang, B., Steane, D.: Clustering Algorithms for ITS Sequence Data with Alignment Metrics. In: Sattar, A., Kang, B.-H. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1027–1031. Springer, Heidelberg (2006)
29. Khandoker, A., Jelinek, H., Palaniswami, M.: Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis. BioMedical Engineering OnLine 8 (2009), http://www.biomedical--engineering--online.com/content/8/1/3
30. Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Boston (1998)
31. Liu, H., Motoda, H., Setiono, R., Zhao, Z.: Feature selection: An ever evolving frontier in data mining. Journal of Machine Learning Research – Proceedings Track 10, 4–13 (2010)
32. Mandvikar, A., Liu, H., Motoda, H.: Compact Dual Ensembles for Active Learning. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 293–297. Springer, Heidelberg (2004)
33. Melville, P., Mooney, R.: Creating diversity in ensembles using artificial data. Information Fusion 6, 99–111 (2005)
34. Ng, E., Hambly, B., McLachlan, C., Matthews, S., Jelinek, H.: WEKA machine learning classification in identifying autonomic dysfunction parameters associated with ACE insertion/deletion genotypes. In: Proceedings of the IASTED International Conference Biomedical Engineering, BioMed 2012, pp. 161–166 (2012)
35. Richards, D.: A social software/Web 2.0 approach to collaborative knowledge engineering. Information Sciences 179(15), 2515–2523 (2009)
36. Richards, D., Compton, P.: Taking up the situated cognition challenge with ripple down rules. International Journal of Human-Computer Studies 49(6), 895–926 (1998)
37. Richards, D.: Two decades of Ripple Down Rules research. Knowledge Eng. Review 24(2), 159–184 (2009)
38. Seewald, A.K., Fürnkranz, J.: An Evaluation of Grading Classifiers. In: Hoffmann, F., Adams, N., Fisher, D., Guimarães, G., Hand, D.J. (eds.) IDA 2001. LNCS, vol. 2189, pp. 115–124. Springer, Heidelberg (2001)
39. Taylor, M., Richards, D.: Discovering Areas of Expertise from Publication Data. In: Richards, D., Kang, B.-H. (eds.) PKAW 2008. LNCS (LNAI), vol. 5465, pp. 218–230. Springer, Heidelberg (2009)
40. Ting, K., Witten, I.: Stacking bagged and dagged models. In: Fourteenth International Conference on Machine Learning, pp. 367–375 (1997)
41. Wada, T., Horiuchi, T., Motoda, H., Washio, T.: A description length-based decision criterion for default knowledge in the ripple down rules method. Knowledge and Information Systems 3(2), 146–167 (2001)
42. Webb, G.: Multiboosting: A technique for combining boosting and wagging. Machine Learning 40, 159–196 (2000)
43. Wolpert, D.: The lack of a priori distinctions between learning algorithms. Neural Computation 8, 1341–1390 (1996)
44. Wolpert, D.: Stacked generalization. Neural Networks 5, 241–259 (1992)
45. Xu, H., Hoffmann, A.: RDRCE: Combining Machine Learning and Knowledge Acquisition. In: Kang, B.-H., Richards, D. (eds.) PKAW 2010. LNCS (LNAI), vol. 6232, pp. 165–179. Springer, Heidelberg (2010)
46. Yearwood, J., Webb, D., Ma, L., Vamplew, P., Ofoghi, B., Kelarev, A.: Applying clustering and ensemble clustering approaches to phishing profiling. In: Data Mining and Analytics 2009, Proc. 8th Australasian Data Mining Conference: AusDM 2009, CRPIT, vol. 101, pp. 25–34 (2009)
47. Yearwood, J., Kang, B., Kelarev, A.: Experimental investigation of classification algorithms for ITS dataset. In: Pacific Rim Knowledge Acquisition Workshop, PKAW 2008, Hanoi, Vietnam, December 15-16, pp. 262–272 (2008)
48. Yoshida, T., Wada, T., Motoda, H., Washio, T.: Adaptive Ripple Down Rules method based on minimum description length principle. Intell. Data Anal. 8(3), 239–265 (2004)