{"title":"Data Mining, Neural Networks and Rule Extraction; IEEE CI Distinguish Lecture","authors":"J. Zurada, Samuel T. Fife","doi":"10.1109/NEUREL.2006.341159","DOIUrl":null,"url":null,"abstract":"This lecture was held during the IEEE SCG CI Chapter meeting. The lecture was open to all members of IEEE. This event is sponsored by Computational Intelligence Society under its Distinguished Lecturer Program. SUMMARY: The opening part of the talk introduces basic premises of data mining. It is shown how numerous paradigms of neurocomputing that are data-driven modeling, feature extraction, dimensionality reduction, visualization, knowledge extraction and logic rule discovery prove useful and effective for data mining. Such modeling, however, often involves handling of heterogenous, subjective, imprecise and noisy data. The second part of the presentation outlines the concept of dimensionality reduction of input data vectors. This technique leads to reduced models achieved through evaluation of sensitivity matrices of perceptron networks. When developing reduced models it is also useful to eliminate underutilized internal weights and also neurons via pruning techniques. The concluding part of the talk reviews the capabilities of perceptron networks for producing understandable IF-THEN rules. Logic rule extraction via neural networks evaluation is discussed and illustrated with examples. SPEAKER: Dr. Jacek M. Zurada is the S.T. Fife Alumni Professor of Electrical and Computer Engineering at the University of Louisville, Louisville, Kentucky, USA. He is the author of the 1992 PWS text Introduction to Artificial Neural Systems, co-editor of the 1994 IEEE Press volume Computational Intelligence. Imitating Life, and of the 2000 MIT Press book Knowledge Based Neurocomputing. He is also the author or co-author of more than 270 journal and conference papers in the area of neural networks, computational intelligence, and data analysis. Dr. Zurada has received a number of awards for distinction in research and teaching, including the 1993 Presidential Award for Research, Scholarship and Creative Activity. In 1998-2003 Dr. Zurada was the Editor-in-Chief of IEEE Transactions on Neural Networks. In 2004-05 he served as the IEEE Computational Intelligence Society President. He is an IEEE Fellow and NNS Distinguished Speaker.","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2006.341159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This lecture was held during the IEEE SCG CI Chapter meeting. The lecture was open to all members of IEEE. This event is sponsored by Computational Intelligence Society under its Distinguished Lecturer Program. SUMMARY: The opening part of the talk introduces basic premises of data mining. It is shown how numerous paradigms of neurocomputing that are data-driven modeling, feature extraction, dimensionality reduction, visualization, knowledge extraction and logic rule discovery prove useful and effective for data mining. Such modeling, however, often involves handling of heterogenous, subjective, imprecise and noisy data. The second part of the presentation outlines the concept of dimensionality reduction of input data vectors. This technique leads to reduced models achieved through evaluation of sensitivity matrices of perceptron networks. When developing reduced models it is also useful to eliminate underutilized internal weights and also neurons via pruning techniques. The concluding part of the talk reviews the capabilities of perceptron networks for producing understandable IF-THEN rules. Logic rule extraction via neural networks evaluation is discussed and illustrated with examples. SPEAKER: Dr. Jacek M. Zurada is the S.T. Fife Alumni Professor of Electrical and Computer Engineering at the University of Louisville, Louisville, Kentucky, USA. He is the author of the 1992 PWS text Introduction to Artificial Neural Systems, co-editor of the 1994 IEEE Press volume Computational Intelligence. Imitating Life, and of the 2000 MIT Press book Knowledge Based Neurocomputing. He is also the author or co-author of more than 270 journal and conference papers in the area of neural networks, computational intelligence, and data analysis. Dr. Zurada has received a number of awards for distinction in research and teaching, including the 1993 Presidential Award for Research, Scholarship and Creative Activity. In 1998-2003 Dr. Zurada was the Editor-in-Chief of IEEE Transactions on Neural Networks. In 2004-05 he served as the IEEE Computational Intelligence Society President. He is an IEEE Fellow and NNS Distinguished Speaker.