A novel Supervised Instance Selection algorithm

International Journal of Business Intelligence and Data Mining

Volumn 2, Issue 4, 2007, Pages 471-495

  Sane, Shirish S ^a   Ghatol, Ashok A ^b  

^a K K WAGH INSTITUTE OF ENGINEERING EDUCATION AND RESEARCH   (India)

^b DR BABASAHEB AMBEDKAR TECHNOLOGICAL UNIVERSITY   (India)

Author keywords

Classification; Data mining; Data reduction; Instance selection; Neural networks

Indexed keywords

EID: 48649106049     PISSN: 17438187     EISSN: 17438195     Source Type: Journal    
DOI: 10.1504/IJBIDM.2007.016384     Document Type: Article

References (31)

1. Aha, D.W., Kibler, D. and Albert, M.K. (1991) 'Instance-based learning algorithms', Machine Learning, Vol. 6, pp.37-66.
2. Andrews, R., Diederich, J. and Tickle, A. (1995) 'A survey and critique of techniques for extracting rules from trained artificial neural networks', Knowledge-Based Systems, Vol. 8, No. 6, pp.373-389.
3. Batista, G. and Monard, M. (2003) 'An analysis of four missing data treatment methods for supervised learning', Applied Artificial Intelligence, Vol. 17, pp.519-533.
4. Blake, C.L. and Merz, C.J. (2002) UCI Repository of Machine Learning Databases, 19 July, http://www.ics.uci.edu/~mlearn/MLRepository.html.
5. Brighton, H. and Mellish, C. (2002) 'Advances in instance selection for instance-based learning algorithms', Data Mining and Knowledge Discovery, Kluwer Academic Publishers, Hingham, MA, USA, Vol. 6, pp.153-172.
6. Cameron-Jones, R.M. (1995) 'Instance selection by encoding length heuristic with random mutation hill climbing', Proceedings of 8th Australian Joint Conference on Artificial Intelligence, Canberra, Australia, pp.99-106.
7. Craven, M. and Shalvik, J. (1997) 'Using neural networks for data mining', Future Generation Computer Systems, Vol. 13, Nos. 2-3, pp.211-229.
8. Gallant, S.I. (1988) 'Connectionist expert systems', Communications of ACM, Vol. 31, pp.152-169.
9. Gates, G.W. (1972) 'The reduced nearest neighbor rule', IEEE Transactions on Information Theory, Vol. IT-18, No. 3, pp.431-433.
10. Han, J. and Kamber, M. (2001) Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, CA.
11. Hart, P.E. (1968) 'The condensed nearest neighbor rule', IEEE Transactions on Information Theory, Vol. 14, pp.515-516.
12. Jimenez, D. (1998) 'Dynamically weighted ensemble neural networks for classification', Proc. IEEE Int'l Joint Conf. on Neural Networks, Anchorage, AK, Vol. 1, pp.753-756.
13. Kantardzic, M. (2003) Data Mining: Concepts, Models, Methods and Algorithms, John Wiley & Sons Inc., USA.
14. Last, M. and Maimon, O. (2004) 'A compact and accurate model for classification', IEEE Transactions on Knowledge and Data Engineering Vol. 16, No. 6, pp.203-242.
15. Li, Y., Shiu, S. and Pal, S. (2006) 'Combining feature reduction and case selection in building CBR classifiers', IEEE Trans. on KDE, Vol. 18, No. 3, pp.415-429.
16. Liu, H. and Motoda, H. (2002) 'On issues of instance selection', Data Mining and Knowledge Discovery, Kluwer Academic Publishers, Hingham, MA, USA, Vol. 6, pp.115-130.
17. Morring, B. and Martinez, T. (2004) 'Weighted instance typicality search (WITS): A nearest neighbor data reduction algorithm', Intelligent Data Analysis, Vol. 8, No. 1, pp.61-78.
18. Oates, T. and Jensen, D. (1997) 'The effects of training set size on decision tree complexity', Proceedings of International Conference on Machine Learning (ICML), pp.254-262.
19. Ritter, G.L., Woodruff, H.B., Lowry, S.R. and Isenhour, T.L. (1975) 'An algorithm for a selective nearest neighbor decision rule', IEEE Transactions on Information Theory, Vol. 21, No. 6, pp.665-669.
20. Roiger, R.J. and Geatz, M.W. (2003) Data Mining: A Tutorial-based Primer, Pearson Education. Delhi, India.
21. Sane, S.S. and Ghatol, A.A. (2006a) 'Using neural networks for accurate prediction of missing values', 2nd International Conference on Advanced Database, San Diego, CA, USA, pp.211-215.
22. Sane, S.S. and Ghatol, A.A. (2006b) 'Effective data mining using neural networks with instance typicality', International Journal of Systemics, Cybernetics and Informatics (IJSCI), Pentagram Research Publication, Hyderabad, India, pp.53-59.
23. Sane, S.S. and Ghatol, A.A. (2006c) 'Using instance typicality to build compact and accurate neural network classifiers', 1st IEEE International Conference on Digital Information Management, Bangalore, India, pp.234-239.
24. Sane, S.S. and Ghatol, A.A. (2006d) 'Use of instance typicality for efficient detection of outliers with neural network classifiers', 9th IEEE International Conference on Information Technology, Bhubneshwar, India, pp.225-228.
25. Taha, A. and Ghosh, J. (1999) 'Symbolic interpretation of artificial neural networks', IEEE Transactions on Knowledge and Data Engineering Vol. 11, No. 3, pp.448-463.
26. Wilson, D. and Martinez, T. (2000) 'Reduction techniques for instance-based learning algorithms', Machine Learning, Kluwer Academic Publishers, Boston, Vol. 38, No. 3, pp.257-286.
27. Witten, H. and Frank, E. (2005) Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., Morgan Kaufmann Publishers, San Francisco, CA.
28. Yu, L., Wang, S. and Lai, K. (2006) 'An integrated data preparation scheme for neural network data analysis', IEEE Trans. KDE, Vol. 18, No. 2, pp.217-230.
29. Zhang, J. (1992) 'Selecting typical instances in instance-based learning', Proceedings of 9th International Conference on Machine Learning Workshop, Morgan Kauffman, Aberdeen, Scotland, San Mateo, CA, pp.470-479.
30. Zhou, Z., Jiang, Y. and Chen, S. (2000) 'A general neural framework for classification rule mining', Int. J. Computers, Systems and Signals Vol. 1, No. 2, pp.154-168.
31. Zhou, Z-H., Jiang, Y. and Chen, S-F. (2003) 'Extracting symbolic rules from trained neural network ensembles', AI Communications, Vol. 16, No. 1, pp.3-15.