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 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.

[1]  Antonio Peregrín,et al.  An Evolutionary Ensemble-Based Method for Rule Extraction with Distributed Data , 2009, HAIS.

[2]  Daniel Rivero,et al.  Automatic Recurrent and Feed-Forward ANN Rule and Expression Extraction with Genetic Programming , 2002, PPSN.

[3]  Madhuri Jha.  ANN-DT : An Algorithm for Extraction of Decision Trees from Artificial Neural Networks , 2013 .

[4]  Jude Shavlik,et al.  THE EXTRACTION OF REFINED RULES FROM KNOWLEDGE BASED NEURAL NETWORKS , 1993 .

[5]  Mark Craven,et al.  Rule Extraction: Where Do We Go from Here? , 1999 .

[6]  Jude W. Shavlik,et al.  Using neural networks for data mining , 1997, Future Gener. Comput. Syst..

[7]  Wang Jian-guo,et al.  Rule extraction from artificial neural network with optimized activation functions , 2008, 2008 3rd International Conference on Intelligent System and Knowledge Engineering.

[8]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[9]  Olcay Boz,et al.  Converting A Trained Neural Network To a Decision Tree DecText - Decision Tree Extractor , 2002, ICMLA.

[10]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[11]  Joachim Diederich,et al.  Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..

[12]  Bart Baesens,et al.  ITER: An Algorithm for Predictive Regression Rule Extraction , 2006, DaWaK.

[13]  Jude W. Shavlik,et al.  in Advances in Neural Information Processing , 1996 .

[14]  Chris Aldrich,et al.  ANN-DT: an algorithm for extraction of decision trees from artificial neural networks , 1999, IEEE Trans. Neural Networks.

[15]  Fei Chen,et al.  LEARNING ACCURATE AND UNDERSTANDABLE RULES FROM SVM CLASSIFIERS , 2004 .

[16]  Bart Baesens,et al.  An empirical investigation into the interpretability of data mining models based on decision trees, tables and rules , 2007 .

[17]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[18]  David McLean,et al.  Rule extraction from neural networks for medical domains , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[19]  Mark Craven,et al.  Extracting comprehensible models from trained neural networks , 1996 .

[20]  James A. Reggia,et al.  Guiding Hidden Layer Representations for Improved Rule Extraction From Neural Networks , 2011, IEEE Transactions on Neural Networks.

[21]  Lars Niklasson,et al.  The Truth is In There - Rule Extraction from Opaque Models Using Genetic Programming , 2004, FLAIRS.