Relevant bibliographies by topics / Rule extraction

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  1. Journal articles
  2. Dissertations / Theses
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Academic literature on the topic 'Rule extraction'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Rule extraction"

1

Grabusts, Peter. "EXTRACTING RULES FROM TRAINED RBF NEURAL NETWORKS." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 18, 2005): 33. http://dx.doi.org/10.17770/etr2005vol1.2128.

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This paper describes a method of rule extraction from trained artificial neural networks. The statement of the problem is given. The aim of rule extraction procedure and suitable neural networks for rule extraction are outlined. The RULEX rule extraction algorithm is discussed that is based on the radial basis function (RBF) neural network. The extracted rules can help discover and analyze the rule set hidden in data sets. The paper contains an implementation example, which is shown through standalone IRIS data set.
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Liu, Yong, Congfu Xu, Qiong Zhang, and Yunhe Pan. "Rough Rule Extracting From Various Conditions: Incremental and Approximate Approaches for Inconsistent Data." Fundamenta Informaticae 84, no. 3-4 (2008): 403–27. https://doi.org/10.3233/fun-2008-843-408.

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Rough rule extraction refers to the rule induction method by using rough set theory. Although rough set theory is a powerful mathematical tool in dealing with vagueness and uncertainty in data sets, it is lack of effective rule extracting approach under complex conditions. This paper proposes several algorithms to perform rough rule extraction from data sets with different properties. Firstly, in order to obtain uncertainty rules from inconsistent data, we introduce the concept of confidence factor into the rule extracting process. Then, an improved incremental rule extracting algorithm is pro
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Pham, D. T., and M. S. Aksoy. "RULES: A simple rule extraction system." Expert Systems with Applications 8, no. 1 (1995): 59–65. http://dx.doi.org/10.1016/s0957-4174(99)80008-6.

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Geczy, Peter, and Shiro Usui. "Fuzzy Rule Acquisition from Trained Artificial Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 5 (1999): 357–67. http://dx.doi.org/10.20965/jaciii.1999.p0357.

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We approach the problem of rule extraction in its primary form. That is, given a trained artificial neural network, we extract rules classifying data set as correctly as possible. Attention is oriented toward extraction of fuzzy rules. The choice of fuzzy rules underlines the aim of balancing rule comprehensibility and complexity. To achieve higher comprehensibility of extracted rules, the formulated theoretical material is an extension of crisp rule extraction 1). A rule extraction algorithm is introduced. The presented algorithm for fuzzy rule extraction implies from the derived theoretical
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MCGARRY, KENNETH, STEFAN WERMTER, and JOHN MACINTYRE. "THE EXTRACTION AND COMPARISON OF KNOWLEDGE FROM LOCAL FUNCTION NETWORKS." International Journal of Computational Intelligence and Applications 01, no. 04 (2001): 369–82. http://dx.doi.org/10.1142/s1469026801000305.

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Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input dimensionality. In such cases, some of the hidden units of the RBF network have a tendency to be "shared" across several output classes or even may not contribute to any output class. To address this we have developed an algorithm called LREX (for Local Rule EXtraction) which tackles these issues by extracting rules at two levels: hREX extracts rules by examining the hidden unit to class assignments while mREX extracts rules based on the input space to output space mappings. The rules extracted by our
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Furuhashi, Takeshi. "Rule Extraction from Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 5 (1999): 339–40. http://dx.doi.org/10.20965/jaciii.1999.p0339.

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Rule extraction from data is one of the key technologies for solving the bottlenecks in artificial intelligence. Artificial neural networks are well suited for representing any knowledge in given data. Extraction of logical/fuzzy rules from the trained artificial neural network is of great importance to researchers in the fields of artificial intelligence and soft computing. Fuzzy rule sets are capable of approximating any nonlinear mapping relationships. Extraction of rules from data has been discussed in terms of fuzzy modeling, fuzzy clustering, and classification with fuzzy rule sets. This
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HAYASHI, YOICHI. "NEURAL NETWORK RULE EXTRACTION BY A NEW ENSEMBLE CONCEPT AND ITS THEORETICAL AND HISTORICAL BACKGROUND: A REVIEW." International Journal of Computational Intelligence and Applications 12, no. 04 (2013): 1340006. http://dx.doi.org/10.1142/s1469026813400063.

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This paper presents theoretical and historical backgrounds related to neural network rule extraction. It also investigates approaches for neural network rule extraction by ensemble concepts. Bologna pointed out that although many authors had generated comprehensive models from individual networks, much less work had been done to explain ensembles of neural networks. This paper carefully surveyed the previous work on rule extraction from neural network ensembles since 1988. We are aware of three major research groups i.e., Bologna' group, Zhou' group and Hayashi' group. The reason of these situ
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S. C. Punitha, S. C. Punitha, Dr P. Ranjit Jeba Thangaiah, and M. Punithavalli M.Punithavalli. "Emulate Rule Extraction From Identical Web Sites Based on Rule Ontology." International Journal of Scientific Research 3, no. 4 (2012): 187–89. http://dx.doi.org/10.15373/22778179/apr2014/64.

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Hayashi, Yoichi, and Naoki Takano. "One-Dimensional Convolutional Neural Networks with Feature Selection for Highly Concise Rule Extraction from Credit Scoring Datasets with Heterogeneous Attributes." Electronics 9, no. 8 (2020): 1318. http://dx.doi.org/10.3390/electronics9081318.

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Convolution neural networks (CNNs) have proven effectiveness, but they are not applicable to all datasets, such as those with heterogeneous attributes, which are often used in the finance and banking industries. Such datasets are difficult to classify, and to date, existing high-accuracy classifiers and rule-extraction methods have not been able to achieve sufficiently high classification accuracies or concise classification rules. This study aims to provide a new approach for achieving transparency and conciseness in credit scoring datasets with heterogeneous attributes by using a one-dimensi
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WATTS, MICHAEL J. "FUZZY RULE EXTRACTION FROM SIMPLE EVOLVING CONNECTIONIST SYSTEMS." International Journal of Computational Intelligence and Applications 04, no. 03 (2004): 299–308. http://dx.doi.org/10.1142/s146902680400132x.

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A method for extracting Zadeh–Mamdani fuzzy rules from a minimalist constructive neural network model is described. The network contains no embedded fuzzy logic elements. The rule extraction algorithm needs no modification of the neural network architecture. No modification of the network learning algorithm is required, nor is it necessary to retain any training examples. The algorithm is illustrated on two well known benchmark data sets and compared with a relevant existing rule extraction algorithm.
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Dissertations / Theses on the topic "Rule extraction"

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Jacobsson, Henrik. "Rule extraction from recurrent neural networks." Thesis, University of Sheffield, 2006. http://etheses.whiterose.ac.uk/6081/.

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Zeranou, Kalliopi. "Template rule development for information extraction: The net method." Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.489539.

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Information Extraction (IE) is becoming increasingly important for the semantic analysis of free-text documents stored in large document repositories, such as the Web. Once free-text is analysed for the recognition of concepts and concept interrelations in events and facts of interest, the resulting structured information becomes a valuable knowledge resource. This resource can be of further use in other information management technologies, such as document summarisation, ontology development, semantic document indexing, question answering, etc., or can be further exploited by data mining and
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Ren, Lu. "Rule extraction from Support Vector Machines : a geometric approach." Thesis, City University London, 2008. http://openaccess.city.ac.uk/11917/.

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Despite the success of connectionist systems in prediction and classification problems, critics argue that the lack of symbol processing and explanation capability makes them less competitive than symbolic systems. Rule extraction from neural networks makes the interpretation of the behaviour of connectionist networks possible by relating sub-symbolic and symbolic processing. However, most rule extraction methods focus only on specific neural network architectures and present limited generalization performance. Support Vector Machine is an unsupervised learning method that has been recently ap
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Andrews, Robert. "An automated rule refinement system." Thesis, Queensland University of Technology, 2003. https://eprints.qut.edu.au/15788/1/Robert_Andrews_Thesis.pdf.

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Artificial neural networks (ANNs) are essentially a 'black box' technology. The lack of an explanation component prevents the full and complete exploitation of this form of machine learning. During the mid 1990's the field of 'rule extraction' emerged. Rule extraction techniques attempt to derive a human comprehensible explanation structure from a trained ANN. Andrews et.al. (1995) proposed the following reasons for extending the ANN paradigm to include a rule extraction facility: * provision of a user explanation capability * extension of the ANN paradigm to 'safety critical' problem d
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Andrews, Robert. "An Automated Rule Refinement System." Queensland University of Technology, 2003. http://eprints.qut.edu.au/15788/.

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Artificial neural networks (ANNs) are essentially a 'black box' technology. The lack of an explanation component prevents the full and complete exploitation of this form of machine learning. During the mid 1990's the field of 'rule extraction' emerged. Rule extraction techniques attempt to derive a human comprehensible explanation structure from a trained ANN. Andrews et.al. (1995) proposed the following reasons for extending the ANN paradigm to include a rule extraction facility: * provision of a user explanation capability * extension of the ANN paradigm to 'safety critical' problem d
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Etchells, Terence Anthony. "Rule extraction from neural networks : a practical and efficient approach." Thesis, Liverpool John Moores University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.402847.

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Rodic, Daniel. "A Hybrid search heuristic-exhaustive search approach for rule extraction." Pretoria : [s.n.], 2000. http://upetd.up.ac.za/thesis/available/etd-05292006-110006/.

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Dancey, Darren. "Tree based methods for rule extraction from artificial neural networks." Thesis, Manchester Metropolitan University, 2008. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493691.

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Nayak, Richi. "GYAN: A methodology for rule extraction from artificial neural networks." Thesis, Queensland University of Technology, 1999. https://eprints.qut.edu.au/36857/6/Richi%20Nayak_Digitised%20Thesis.pdf.

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Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanatio
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Siniakov, Peter [Verfasser]. "GROPUS – an Adaptive Rule-based Algorithm for Information Extraction / Peter Siniakov." Berlin : Freie Universität Berlin, 2008. http://d-nb.info/1022642065/34.

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Books on the topic "Rule extraction"

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D, Friederici Angela, and Menzel Randolf 1940-, eds. Learning: Rule extraction and representation. Walter de Gruyter, 1998.

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Diederich, Joachim, ed. Rule Extraction from Support Vector Machines. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75390-2.

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Joachim, Diederich, ed. Rule extraction from support vector machines. Springer, 2008.

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Ma, Zhe. A heuristic for general rule extraction from a multilayer perceptron. University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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Meloun, M. Computation of solution equilibria: A guide to methods in potentiometry, extraction, and spectrophotometry. Edited by Havel J and Högfeldt Erik 1924-. E. Horwood, 1988.

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K, Kokula Krishna Hari, ed. Entity Mining Extraction Using Sequential Rules: ICIEMS 2014. Association of Scientists, Developers and Faculties, 2014.

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Yang, Y. X. Extracting boolean rules from CA patterns. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

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1977-, Zhao Yanchang, Zhang Chengqi 1957-, and Cao Longbing 1969-, eds. Post-mining of association rules: Techniques for effective knowledge extraction. Information Science Reference, 2009.

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United States. National Aeronautics and Space Administration., ed. DecisionMaker software and extracting fuzzy rules under uncertainty. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1992.

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United States. National Aeronautics and Space Administration., ed. Measuring uncertainty by extracting fuzzy rules using rough sets. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1991.

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Book chapters on the topic "Rule extraction"

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Pang, Shaoning, and Nik Kasabov. "SVMT-Rule: Association Rule Mining Over SVM Classification Trees." In Rule Extraction from Support Vector Machines. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75390-2_6.

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Torrey, Lisa, Jude Shavlik, Trevor Walker, and Richard Maclin. "Rule Extraction for Transfer Learning." In Rule Extraction from Support Vector Machines. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75390-2_3.

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Roy, Asim. "On connectionism and rule extraction." In Perspectives in Neural Computing. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0219-9_31.

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Tsukimoto, Hiroshi. "Rule Extraction from Prediction Models." In Methodologies for Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48912-6_6.

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Johansson, Ulf, Rikard König, Henrik Linusson, Tuve Löfström, and Henrik Boström. "Rule Extraction with Guaranteed Fidelity." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44722-2_30.

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Diederich, Joachim. "Rule Extraction from Support Vector Machines: An Introduction." In Rule Extraction from Support Vector Machines. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75390-2_1.

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He, Jieyue, Hae-jin Hu, Bernard Chen, Phang C. Tai, Rob Harrison, and Yi Pan. "Rule Extraction from SVM for Protein Structure Prediction." In Rule Extraction from Support Vector Machines. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75390-2_10.

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Martens, David, Johan Huysmans, Rudy Setiono, Jan Vanthienen, and Bart Baesens. "Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring." In Rule Extraction from Support Vector Machines. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75390-2_2.

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Fung, Glenn, Sathyakama Sandilya, and R. Bharat Rao. "Rule Extraction from Linear Support Vector Machines via Mathematical Programming." In Rule Extraction from Support Vector Machines. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75390-2_4.

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Núñez, Haydemar, Cecilio Angulo, and Andreu Català. "Rule Extraction Based on Support and Prototype Vectors." In Rule Extraction from Support Vector Machines. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75390-2_5.

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Conference papers on the topic "Rule extraction"

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Chiticariu, Laura, Yunyao Li, and Frederick R. Reiss. "Rule-Based Information Extraction is Dead! Long Live Rule-Based Information Extraction Systems!" In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2013. http://dx.doi.org/10.18653/v1/d13-1079.

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Alcaraz, Benoît, Adam Kaliski, and Christopher Leturc. "An A-Star Algorithm for Argumentative Rule Extraction." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013110400003890.

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Khosravi R, Hossein, M. H. Yaghmaee Moghaddam, Amirhossein Shahroudi, and Hadi Sadoghi Yazdi. "FCM-fuzzy rule base: A new rule extraction mechanism." In 2011 International Conference on Innovations in Information Technology (IIT). IEEE, 2011. http://dx.doi.org/10.1109/innovations.2011.5893829.

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Xu, E. "An Algorithm for Rule Extraction." In TENCON 2006 - 2006 IEEE Region 10 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tencon.2006.343804.

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Mi, Haitao, and Liang Huang. "Forest-based translation rule extraction." In the Conference. Association for Computational Linguistics, 2008. http://dx.doi.org/10.3115/1613715.1613745.

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Hayashi, Yoichi, Ryusuke Sato, and Sushmita Mitra. "A new approach to three ensemble neural network rule extraction using recursive-rule extraction algorithm." In 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas). IEEE, 2013. http://dx.doi.org/10.1109/ijcnn.2013.6706823.

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Cepukenas, Julius, Chenghua Lin, and Derek Sleeman. "Applying Rule Extraction & Rule Refinement techniques to (Blackbox) Classifiers." In K-CAP 2015: Knowledge Capture Conference. ACM, 2015. http://dx.doi.org/10.1145/2815833.2816950.

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Ruskanda, Fariska Zakhralativa, Dwi Hendratmo Widyantoro, and Ayu Purwarianti. "Sequential Covering Rule Learning for Language Rule-based Aspect Extraction." In 2019 International Conference on Advanced Computer Science and information Systems (ICACSIS). IEEE, 2019. http://dx.doi.org/10.1109/icacsis47736.2019.8979743.

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Wei, Wu, Shengsheng Shi, Yulong Liu, Haitao Wang, Chunfeng Yuan, and Yihua Huang. "Extraction Rule Language for Web Information Extraction and Integration." In 2013 10th Web Information System and Application Conference (WISA). IEEE, 2013. http://dx.doi.org/10.1109/wisa.2013.21.

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Azcarraga, Arnulfo, Michael David Liu, and Rudy Setiono. "Keyword extraction using backpropagation neural networks and rule extraction." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252618.

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Reports on the topic "Rule extraction"

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McCarthy, James, Jeffrey Panek, and Tom McGrath. PR-312-12206-R02 FTIR Formaldehyde Measurement at Turbine NESHAP and Ambient Levels. Pipeline Research Council International, Inc. (PRCI), 2018. http://dx.doi.org/10.55274/r0011476.

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When natural gas is combusted, formaldehyde is formed as an intermediate product as methane is converted to CO2 during combustion. Formaldehyde is regulated by the U.S. EPA as a hazardous air pollutant (HAP) under National Emission Standards for Hazardous Air Pollutants (NESHAP) regulations, and both turbines and reciprocating engines are listed source categories where EPA is required to develop regulations. NESHAPs have been adopted for natural gas-fired combustion turbines and reciprocating internal combustion engines (RICE), with initial regulations in 2004 that included a 91 parts per bill
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