Rule extraction from ensemble methods using aggregated decision trees

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

Volumn 7664 LNCS, Issue PART 2, 2012, Pages 599-607

  Iqbal, Md Ridwan Al ^a  

^a AMERICAN INTERNATIONAL UNIVERSITY BANGLADESH   (Bangladesh)

Author keywords

Decision Tree; Ensemble Learning; Rule Extraction

Indexed keywords

ENSEMBLE LEARNING; ENSEMBLE METHODS; ENSEMBLES OF CLASSIFIERS; PATTERN RECOGNITION ALGORITHMS; RULE EXTRACTION; RULE EXTRACTION METHOD;

DATA PROCESSING; LEARNING ALGORITHMS; PATTERN RECOGNITION;

DECISION TREES;

EID: 84869071460     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-34481-7_73     Document Type: Conference Paper

References (16)

1. Nayak, R.: Gyan: A methodology for rule extraction from artificial neural networks. Queensland University of Technology, Brisbane, Tech. Rep. (2003)
2. Durbari, A.: Rule extraction from trained ann: A survey. ECS (2001)
3. Jacobsson, H.: Rule extraction from recurrent neural networks: A taxonomy and review. Neural Computation 17(6), 1223-1263 (2005)
4. Andrews, R., Diederich, J., Tickle, A.B.: A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8(6), 373-389 (1995)
5. Craven, M.W., Shavlik, J.W.: Using sampling and queries to extract rules from trained neural networks. In: Machine Learning: Proceedings of the Eleventh International Conference, San Francisco, CA, USA (1994)
6. Thrun, S.: Extracting rules from artificial neural networks with distributed representations. In: Advances in Neural Information Processing Systems, vol. 7. MIT Press, Cambridge (1995)
7. Craven, M.W., Shavlik, J.W.: Extracting tree-structured representations of trained networks. In: Advances in Neural Information Processing Systems, vol. 8. MIT Press, Cambridge (1996)
8. Hansen, L., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 993-1001
9. Hashem, S.: Optimal linear combinations of neural networks. Neural Networks 10, 599-614
10. Opitz, D., Maclin, R.: Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 169-198
11. Breiman, L.: Bagging predictors. Machine Learning, 123-140 (1996)
12. Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, Bari, Italy, pp. 148-156 (1996)
13. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Annals of Statistics, 337-407
14. Pazzani, M., Brunk, C., Silverstein, G.: A knowledge-intensive approach to learning relational concepts. In: Proceedings of the Eighth International Workshop on Machine Learning, San Francisco, pp. 432-436 (1991)
15. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
16. Brayton, R.K., Hachtel, G.D., McMullen, C., Sangiovanni-Vincentelli, A.L.: Logic Minimization Algorithms for VLSI Synthesis. Springer