KDRuleEx: A Novel Approach for Enhancing User Comprehensibility Using Rule Extraction

Knowledge representation opaque model like ANN has advantage of accuracy and limitation of interpretability. Transparent models like decision tree, decision table and rules represent knowledge in more understandable form and can easily be integrated with other learning system. Converting an opaque m...

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Vydáno v:2012 Third International Conference on Intelligent Systems Modelling and Simulation s. 55 - 60
Hlavní autoři: Sethi, K. K., Mishra, D. K., Mishra, B.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.02.2012
Témata:
Accuracy
ANN
Artificial neural networks
Classification algorithms
Comprehensibility
Data mining
Data models
Decision Table
Decision trees
Fidelity
Rule Extraction
Training
ISBN:1467308862, 9781467308861
ISSN:2166-0662
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Shrnutí:Knowledge representation opaque model like ANN has advantage of accuracy and limitation of interpretability. Transparent models like decision tree, decision table and rules represent knowledge in more understandable form and can easily be integrated with other learning system. Converting an opaque model like ANN to transparent model is called rule extraction. Lot of work has been done in the field of rule extraction like "SVM to Rules", "ANN to Decision Tree", "ANN to Rules" etc. We did not find any direct approach of converting ANN to decision table in our literature survey. In this paper, we proposed a novel pedagogical rule extraction technique to generate a decision table using training example set and a trained artificial neural network on it. The proposed algorithm can be used with both discrete as well as continuous input. The proposed algorithm has advantage in terms of computational performance and memory management.
ISBN:1467308862
9781467308861
ISSN:2166-0662
DOI:10.1109/ISMS.2012.116