The week's pick
Random Articles
Reseach Article
Extended Taxonomy of Rule Extraction Techniques and Assessment of KDRuleEx
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 50 - Number 21 |
| Year of Publication: 2012 |
| Authors: Kamal Kumar Sethi, Durgesh Kumar Mishra, Bharat Mishra |
10.5120/7928-1236
|
Kamal Kumar Sethi, Durgesh Kumar Mishra, Bharat Mishra . Extended Taxonomy of Rule Extraction Techniques and Assessment of KDRuleEx. International Journal of Computer Applications. 50, 21 ( July 2012), 25-31. DOI=10.5120/7928-1236
Abstract
Classifiers like ANN & SVM are always preferred over other classification model like decision tree due to higher accuracy but lacking explainability and comprehensibility. Rule extraction techniques bridges gap between accuracy and comprehensibility. To evaluate and compare different rule extraction techniques, we require measures for evaluation and categorization. Taxonomy helps us to select a technique based on the requirements and desired priorities. In this paper, we extended popular ADT-taxonomy of rule extraction which has been designed for ANN as underlying model. Proposed taxonomy covers all types of work related to rule extraction. It makes easier to introduce new rule extraction techniques by improving the performance on evaluation criteria. In this paper almost all possible aspects of evaluation and categorization of rule extraction techniques has been considered and further used to evaluate the algorithm KDRuleEx.
References
- B. Baesens, T. V. Gestel, S. Viaene, M. Stepanova, J. Suykens and J. Vanthienen, "Benchmarking state of the art classification algorithms for credit scoring," vol. 56, no. 6, pp. 627-635, 2003.
- U. Johansson, R. Konig and L. Niklasson, "Automatically balancing accuracy and comprehensibility in predictive modeling," 2005.
- S. e. a. Thrun, "The MONK's problems: A performance comparison of different learning algorithms," Pittsburgh, pp. 91-197, 1991.
- T. Löfström and P. Odqvist, "RULE EXTRACTION IN DATA MINING - FROM A META LEARNING PERSPECTIVE," 2004.
- J. Diederich, A. B. Tickle and S. Geva, "Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks," vol. 262, pp. 479-490, 2010.
- R. Andrews, J. Diederich and A. Tickle, "Survey and critique of techniques for extracting rules from trained artificial neural networks," Knowledge Based Systems, vol. 8, no. 6, pp. 373-389, 1995.
- R. Kohavi, "The Power of Decision Tables," 1995.
- J. Huysmans, K. Dejaeger, C. Mues, J. Vanthienen and B. Baesens, "An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models," vol. 51, no. 1, pp. 141-154, 2011.
- J. Vanthienen, "A more general comparison of the decision table and tree: A response," Communications of the ACM, vol. 37, no. 2, pp. 109-113, 1992.
- K. K. Sethi, D. K. Mishra and B. Mishra, "KDRuleEx: A Novel Approach for Enhancing User Comprehensibility Using Rule Extraction," in Third International Conference Intelligent Systems, Modelling and Simulation, 2012.
- M. W. Craven and J. E. Shavlik, "Rule extraction: Where do we go from where?," 1999.
- A. Darbari, "Rule Extration from Trained ANN : ASurvey," TU Dresden, 2001.
- S. K. Ahamed, "Survey of rule extraction methods," ETD Collection for Wayne State University, 2004.
- M. G. Augasta and T. Kathirvalavakumar, "Rule extraction from neural networks — A comparative study," in International Conference on , 2012.
- J. Huysmans, B. Baesens and J. Vanthienen, "Using rule extraction to improve the comprehensibility of predictive models," Faculty of Economics and Applied Economics, 2006.
- W. Duch, R. Setiono and J. M. Zurada, "Computational intelligence methods for rule-based data understanding," 2004.
- A. Tickle, R. Andrews, M. Golea and J. Diederich, "The truth will come to light: directions and challenges in extracting the knowledge embedded within mined artificial neural networks," IEEE Transactions on Neural Networks, vol. 9, no. 6, p. 1057–1068, 1998.
- G. Towell and J. Shavlik, "The extraction of refined rules from knowledge based neural networks," Machine Learning, vol. 13, no. 1, pp. 71-101, 1993.
- M. W. Craven and J. W. Shavlik, "Extracting tree-structured representations of trained networks," Advances in Neural Information Processing Systems, vol. 8, pp. 24-30, 1996.
- G. P. Schmitz, C. Aldrich and F. S. Gouws, "ANN-DT: An algorithm for extraction of decision trees from artificial neural networks," IEEE Transactions on Neural Networks, vol. 10, no. 6, pp. 1392-1401, 1999.
- A. Tickle, M. Orlowski and J. Diederich, "DEDEC: a methodology for extracting rule from trained artificial neural networks," in Workshop on Rule Extraction from Trained Neural Networks, Brighton, UK, 1996.
- M. Golea, "On the complexity of rule extraction from neural networks and network querying," in Rule Extraction From Trained Artificial Neural Networks Workshop, Brighton, UK, 1996.
- Z. H. Zhou, "Rule extraction: using neural networks or for neural networks?," Journal of Computer Science and, vol. 19, no. 2, pp. 249-253, 2004.
- P. Domingos, "The Rule of Occam's Razor in Knowledge Discovery," Data Min. Knowl. Discov, vol. 3, no. 4, pp. 409-425, 1999.
- U. Johansson, T. Lofstrom, R. Konig, C. Sonstrod and L. Niklasson, "Rule Extraction from Opaque Models- A Slightly Different Perspective," 2006.
- R. Kohavi and F. Provost, "Glossary of Terms," Special Issue on Applications of Machine Learning and the Knowledge Discovery Process, vol. 30, pp. 271-274, 1998.
- H. Jacobsson, "Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review," vol. 17, no. 6, pp. 1-37, 2005.
- H. Zhang, Y. Zhang and H. Lin, "A comparison study of impervious surfaces estimation using optical and SAR remote sensing images," pp. 148-156 , 2012.
- R. König, "Predictive Techniques and Methods for Decision Support in Situations with Poor Data Quality," University of Borås. School of Business and Informatics, Sweden, 2009.
- C. Mues, J. Huysmans, B. Baesens and J. Vanthienen, "An empirical investigation into the interpretability of data mining models based on decision trees, tables and rules," in 22nd European Conference on Operational Research, Prague, 2007.
- A. M. Moreno Garcia, M. Verhelle and J. Vanthienen, "An overview of decision table literature 1982-2000," in organized by the Research Group on AI in Accounting , Huelva, Spain, 2000.
- M. Pennington, "C4. 5 Rule Preceded by an Artificial Neural Network Ensemble for Medical Diagnosis," 2003.
- A. Kalousis, J. Gama and M. Hilario, "On Data and Algorithms: Understanding Inductive Performance," Special Issue on Meta-Learning, vol. 54, no. 3, pp. 275-312, 2004.
- T. Löfström and U. Johansson, "Predicting the Benefit of Rule Extraction - A Novel Component in Data Mining," vol. 7, no. 3, pp. 78-108, 2005.
- N. Barakat and J. Diederich, "Eclectic rule-extraction from support vector machines," vol. 2(1), pp. 59-62, 2005.
- R. KÄonig and L. Niklasson, "Automatically balancing accuracy and comprehensibility in predictive modeling," 2005.
- U. Johansson, R. König and L. Niklasson, "The Truth is in There: Rule Extraction from Opaque Models Using Genetic Programming," in 17th Florida Artificial Intelligence Research Symposium (FLAIRS), Miami, FL, 2004.
Index Terms
Keywords