Is it worth generating rules from neural network ensembles?

Andrews, 1994, Rule extraction from a constrained error back-propagation multi-layer perceptron, Proc. of the Australian Conference on Neural Networks, 9 Andrews, 1995, Survey and critique of techniques for extracting rules from trained artificial neural networks, Knowledge Based Systems, 8, 373, 10.1016/0950-7051(96)81920-4 Andrews, 2002, Rule extraction from local cluster neural nets, Neurocomput., 47, 1, 10.1016/S0925-2312(01)00577-X Bairoch, 2000, The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000, Nucl. Acids Res., 28, 45, 10.1093/nar/28.1.45 Bauer, 1999, An empirical comparison of voting classification algorithms: bagging, boosting, and variants, Machine Learning, 36, 105, 10.1023/A:1007515423169 Baxt, 1992, Improving the accuracy of an artificial neural network using multiple differently trained networks, Neural Comput., 4, 772, 10.1162/neco.1992.4.5.772 Blake Bologna, 1998, Rule extraction from modular transparent boxes, 393 Bologna, 1998, Rule extraction from the oblique multi-layer perceptron, Proc. of the Australian Conference on Neural Networks, 260 Bologna, 2000, Symbolic rule extraction from the DIMLP neural network, 240 Bologna, 2001, A study on rule extraction from several combined neural networks, Internat. J. Neural Syst., 11, 247, 10.1142/S0129065701000680 Bologna, 2003, A model for single and multiple knowledge based networks, Artificial Intelligence in Medicine, 28, 141, 10.1016/S0933-3657(03)00055-1 Breiman, 1996, Bagging predictors, Machine Learning, 26, 123, 10.1007/BF00058655 Condorcet, 1784, Sur les elections par scrutiny, Histoire de l'Academie Royale des Sciences, 31 Craven, 1996, Extracting tree-structured representations of trained networks, Proc. Neural Inform. Process. Syst., 8, 24 M. Craven, Extracting comprehensible models from trained neural networks, PhD Thesis, University of Wisconsin, 1996 Domingos, 1998, Discovery via multiple models, Intelligent Data Anal., 2, 187, 10.1016/S1088-467X(98)00023-7 Duch, 2001, A new methodology of extraction, optimization and application of crisp and fuzzy logical rules, Trans. Neural Networks, 12, 277, 10.1109/72.914524 Fu, 1991, Rule learning by searching on adapted nets, 590 Gallant, 1998, Connectionist expert systems, Comm. ACM, 31, 152, 10.1145/42372.42377 Garcez, 2001, Symbolic knowledge extraction from trained neural networks: a sound approach, Artificial Intelligence, 125, 155, 10.1016/S0004-3702(00)00077-1 Hertz, 1991 Jang, 1993, ANFIS. Adaptive-network-based fuzzy inference system, Trans. System Man Cybernet., 23, 665, 10.1109/21.256541 Kasabov, 1999, Rule insertion and rule extraction from evolving fuzzy networks: algorithms and applications for building adaptive intelligent expert systems, 1406 Krishnan, 1999, A search technique for rule extraction from trained neural networks, Pattern Recogn. Lett., 20, 273, 10.1016/S0167-8655(98)00145-7 Maurer-Stroh, 2002, N-terminal N-myristoylation of proteins: refinement of the sequence motif and its taxon-specific differences, J. Mol. Biol., 317, 523, 10.1006/jmbi.2002.5425 Muggleton, 1992, Predicting protein secondary structure using logic-based machine, Protein Engineering, 5, 647, 10.1093/protein/5.7.647 Nauck, 1999, Obtaining interpretable fuzzy classification rules from medical data, Artificial Intelligence in Medicine, 16, 149, 10.1016/S0933-3657(98)00070-0 Opitz, 1999, Popular ensemble methods: an empirical study, Artificial Intelligence Res., 11, 169, 10.1613/jair.614 Quinlan, 1993 Rosenblatt, 1962 Saito, 1990, Rule extraction from facts and neural networks, 379 Setiono, 1997, Neurolinear: from neural networks to oblique decision rules, Neurocomput., 17, 1, 10.1016/S0925-2312(97)00038-6 Setiono, 2000, Generating concise and accurate classification rules for breast cancer diagnosis, Artificial Intelligence in Medicine, 18, 205, 10.1016/S0933-3657(99)00041-X Sigrist, 2002, PROSITE: A documented database using patterns and profiles as motif descriptors, Brief Bioinform., 3, 265, 10.1093/bib/3.3.265 Taha, 1999, Symbolic interpretation of artificial neural networks, Trans. Neural Networks, 11, 448 S. Thrun, et al., The MONK's problems—a performance comparison of different learning algorithms, Technical Report CS-CMU-91-197, Carnegie Mellon University, 1991 Thrun, 1995, Extracting rules from artificial neural networks with distributed representations, 505 G. Towell, Symbolic knowledge and neural networks: insertion, refinement and extraction, PhD Thesis, University of Wisconsin, 1991 Towell, 1993, The extraction of refined rules from knowledge based neural networks, Machine Learning, 131, 71, 10.1007/BF00993103 Wall, 2003, Explaining the output of ensembles in medical decision support on a case by case basis, Artificial Intelligence in Medicine, 28, 141, 10.1016/S0933-3657(03)00056-3 P. Werbos, Beyond regression: new tools for prediction and analysis in the behavioral sciences, PhD Thesis, Harvard University, 1974 Wolpert, 1997, The no free lunch theorems, IEEE Trans. Evolutionary Comput., 1, 67, 10.1109/4235.585893 Zadeh, 1965, Fuzzy sets, Inform. Control, 8, 338, 10.1016/S0019-9958(65)90241-X Zhou, 2003, Extracting symbolic rules from trained neural network ensembles, AI Commun., 16, 3