Rule extraction from linear combinations of DIMLP neural networks

Proceedings - Brazilian Symposium on Neural Networks, SBRN

Volumn 2000-January, Issue , 2000, Pages 95-100

  Bologna, G ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Artificial neural networks; Computational complexity; Computer networks; Data mining; Decision trees; Neural networks; Neurons; NP hard problem; Polynomials; Taxonomy

Indexed keywords

COMPLEX NETWORKS; COMPUTATIONAL COMPLEXITY; COMPUTER NETWORKS; DATA MINING; DECISION TREES; EXTRACTION; FORESTRY; NEURAL NETWORKS; NEURONS; POLYNOMIAL APPROXIMATION; POLYNOMIALS; TAXONOMIES; TREES (MATHEMATICS);

C4.5 DECISION TREES; DEGREE OF MATCHING; LINEAR COMBINATIONS; NETWORK RESPONSE; POLYNOMIAL-TIME; PUBLIC DOMAINS; RULE EXTRACTION; RULE EXTRACTION FROM NEURAL NETWORKS;

MULTILAYER NEURAL NETWORKS;

EID: 84950244693     PISSN: 15224899     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SBRN.2000.889720     Document Type: Conference Paper

References (16)

1. Andrews, R., Diederich, J., Tickle, A.B.: Survey and Critique of Techniques for Extracting Rules from Trained Artificial Neural Networks. Knowledge-Based Systems, vol. 8, no. 6 (1995), 373-389.
2. Bologna, G., Pellegrini, C.: Symbolic Rule Extraction from Modular Transparent Boxes. In Proc. of the Conference of Neural Networks and their Applications (1998), 393-398.
3. Bologna, G.: Symbolic Rule Extraction from the DIMLP Neural Network. In Neural Hybrid Systems, Wermter, S., Sun, R. (eds.), Springer Verlag (2000), 240-254.
4. Bologna, G.: Rule Extraction from a Multi Layer Perceptron with Staircase Activation Functions. Accepted to the International Joint Conference on Neural Networks (2000).
5. Breiman, L.: Bagging Predictors. Machine Learning (1996), 24, 123-140.
6. Breiman, L.: Prediction Games and arcing Algorithms. Technical Report 504 (1998), Department of Statistics, University of California, Berkeley.
7. Craven, M., Shavlik, J.: Rule Extraction: Where Do We Go From Here ? University of Wisconsin, Machine Learning Research Group (1999), Working Paper 99-1.
8. Feng, C., Sutherland, A., King, S., Muggleton, S., Henery, R.: Comparison of Machine Learning Classifiers to Statistics and Neural Networks. Proc. of AI and Stats Conference (1993).
9. Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Ann. Eugenics, (1936) 7 (2), 179-188.
10. Golea, M.: On the Complexity of Rule Extraction from Neural Networks and Network Querying. Proc. of the Rule Extraction from Trained Artificial Neural Networks Workshop, Society for The Study of Artificial Intelligence and Simulation of Behaviour, Workshop Series ( AISB'96). University of Sussex, Brighton, UK (1996), 51-59.
11. Gorman, R.P., Sejnowski, T.J.: Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets. Journal of Neural Networks (1988), 1(1) 75-88.
12. Opitz, D., Maclin, R.: Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research (1999), 11, 169-198.
13. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufman (1993).
14. Quinlan, J.R.: MiniBoosting Decision Trees. Journal of Artificial Intelligence Research, (1998).
15. Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., Johannes R.S.: Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. In Proc. of the Symposium on Computer Applications and Medical Care (1988), IEEE Computer Society Press, 261-265.
16. Webb, G.I.: Further Experimental Evidence Against the Utility of Occam's Razor. Journal of Artificial Intelligence Research (1996), 4, 397-417.