Simple hybrid and incremental postpruning techniques for rule induction

IEEE Transactions on Knowledge and Data Engineering

Volumn 25, Issue 2, 2013, Pages 476-480

  Shehzad, K ^a  

^a UNIVERSITY OF ENGINEERING AND TECHNOLOGY   (Pakistan)

Author keywords

classification; data mining; inductive learning; knowledge discovery; machine learning; noise handling; Overfitting; pruning; rule induction; supervised learning

Indexed keywords

INDUCTIVE LEARNING; NOISE HANDLING; OVERFITTING; PRUNING; RULE INDUCTION;

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL METHODS; DATA MINING; INFORMATION SYSTEMS; SUPERVISED LEARNING;

LEARNING SYSTEMS;

EID: 84871823645     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2011.237     Document Type: Article

References (19)

1. F. Klawonn and F. Rehm, "Clustering Techniques for Outlier Detection," Encyclopedia of Data Warehousing and Mining, E. Brennan, A. Bubnis, R. Davies, and S. VanderHook eds., vol. 1, pp. 397-402, Idea Group Publishing, 2006.
2. X. Yin and J. Han, "CPAR: Classification Based on Predictive Association Rules," Proc. SIAM Int'l Conf. Data Mining, pp. 345-360, 2003.
3. D. T. Pham, S. Bigot, and S. S. Dimov, "A Rule Merging Technique for Handling Noise in Inductive Learning," Proc. Inst. of Mechanical Engineers, Part C: J. Mechanical Eng. Science, vol. 218, pp. 1255-1268, 2004. (Pubitemid 39520005)
4. O. Dain, R. Cunningham, and S. Boyer, "IREP++, a Faster Rule Learning Algorithm," Proc. Fourth SIAM Int'l Conf. Data Mining, pp. 138-146, 2004.
5. J. Furnkranz and G. Widmer, "Incremental Reduced Error Pruning," Proc. 11th Int'l Conf. Machine Learning, pp. 70-77, 1994.
6. P. Domingos, "Efficient Specific-to-General Rule Induction," Proc. Second Int'l Conf. Knowledge Discovery and Data Mining, pp. 319-322, 1996.
7. P. Domingos, "Linear-Time Rule Induction," Proc. Fourth SIAM Int'l Conf. Data Mining, pp. 96-101, 1996.
8. D. T. Pham and A. A. Afify, "Three New MDL-Based Pruning Techniques for Robust Rule Induction," Proc. Inst. of Mechanical Engineers, Part C: J. Mechanical Eng. Science, vol. 220, pp. 553-564, 2006. (Pubitemid 44964879)
9. D. T. Pham and A. A. Afify, "RULES-6: A Simple Rule Induction Algorithm for Supporting Decision Making," Proc. IEEE 31st Ann. Conf. Industrial Electronics Soc., pp. 2184-2189, 2005. (Pubitemid 44549617)
10. K. Shehzad, "EDISC: A Class-Tailored Discretization Technique for Rule-Based Classification," IEEE Trans. Knowledge and Data Eng., vol. 24, no. 8, pp. 1435-1447, http://dx. doi. org/10. 1109/TKDE. 2011. 101, Aug. 2012.
11. A. A. Afify, "Design and Analysis of Scalable Rule Induction Systems," PhD thesis, Systems Eng. Division, Univ. of Wales, Cardiff, United Kingdom, 2004.
12. S. Bigot, "New Techniques for Handling Continuous Values in Inductive Learning," PhD thesis, Systems Eng. Division, Univ. of Wales, Cardiff, United Kingdom, 2002.
13. D. T. Pham, S. S. Dimov, and S. Bigot, "RULES-5: A Rule Induction Algorithm for Problems Involving Continuous Attributes," Proc. Inst. of Mechanical Engineers, Part C: J. Mechanical Eng. Science, vol. 217, pp. 1273-1286, 2003. (Pubitemid 38086701)
14. C. L. Blake and C. J. Merz, UCI Repository of Machine Learning Databases, Dept. of Information and Computer Science, Univ. of California, Irvine, http://archive. ics. uci. edu/ml/, 1998.
15. I. H. Witten and E. Frank, Data Mining-Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers, 2005.
16. W. W. Cohen, "Fast Effective Rule Induction," Proc. 12th Int'l Conf. Machine Learning, pp. 115-123, 1995.
17. J. Wang and G. Karypis, "On Mining Instance-Centric Classification Rules," IEEE Trans. Knowledge and Data Eng, vol. 18, no. 11, pp. 1497-1511, Nov. 2006.
18. E. Frank and I. H. Witten, "Generating Accurate Rule Sets without Global Optimization," Proc. 15th Int'l Conf. Machine Learning (ICML '98), pp. 144-151, 1998.
19. P. Clark and R. Boswell, "Rule Induction with CN2: Some Recent Improvements," Proc. Fifth European Working Session on Learning, pp. 151-163, 1991.