Automatic Recurrent ANN Rule Extraction with Genetic Programming

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@InProceedings{dorado:ppsn2002:pp485,

• author = "Julian Dorado and Juan R. Rabunal and Antonino Santos and Alejandro Pazos and Daniel Rivero",
• title = "Automatic Recurrent ANN Rule Extraction with Genetic Programming",
• booktitle = "Parallel Problem Solving from Nature - PPSN VII",
• address = "Granada, Spain",
• month = "7-11 " # sep,
• pages = "485--494",
• year = "2002",
• editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel",
• number = "2439",
• series = "Lecture Notes in Computer Science, LNCS",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, Neural Networks",
• ISBN = "3-540-44139-5",
• language = "en",
• oai = "oai:CiteSeerX.psu:10.1.1.205.6971",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.6971",
• URL = "http://sci2s.ugr.es/keel/pdf/specific/congreso/springer1_4.pdf",
• DOI = "doi:10.1007/3-540-45712-7_47",
• size = "10 pages",
• abstract = "Various rule-extraction techniques using ANN have been used so far, most of them being applied on multi-layer ANN, since they are more easily handled. In many cases, extraction methods focusing on different types of networks and training have been implemented. However, there are virtually no methods that view the extraction of rules from ANN as systems which are independent from their architecture, training and internal distribution of weights, connections and activation functions. This paper proposes a rule extraction system of ANN regardless of their architecture (multi-layer or recurrent), using Genetic Programming as a rule-exploration technique.",

}

Genetic Programming entries for Julian Dorado Juan Ramon Rabunal Dopico Antonino Santos del Riego Alejandro Pazos Sierra Daniel Rivero Cebrian

Citations