Data Mining, Neural Networks and Rule Extraction; IEEE CI Distinguish Lecture

J. Zurada, Samuel T. Fife
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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.
数据挖掘、神经网络与规则抽取IEEE CI区分讲座
本次讲座是在IEEE SCG CI分会会议期间举行的。讲座对IEEE的所有成员开放。本活动由计算智能学会杰出讲师计划赞助。讲座的开头部分介绍了数据挖掘的基本前提。它展示了数据驱动建模、特征提取、降维、可视化、知识提取和逻辑规则发现等众多神经计算范式对数据挖掘是如何有用和有效的。然而,这种建模通常涉及处理异质的、主观的、不精确的和有噪声的数据。演示的第二部分概述了输入数据向量降维的概念。这种技术导致通过评估感知器网络的灵敏度矩阵来实现简化模型。在开发约简模型时,通过修剪技术消除未充分利用的内部权值和神经元也很有用。演讲的最后部分回顾了感知器网络产生可理解的IF-THEN规则的能力。讨论了基于神经网络评价的逻辑规则提取方法,并用实例进行了说明。主讲人:Jacek M. Zurada博士是美国肯塔基州路易斯维尔市路易斯维尔大学电气与计算机工程的st . Fife校友教授。他是1992年PWS文本《人工神经系统导论》的作者,也是1994年IEEE出版社《计算智能》卷的共同编辑。模仿生活,以及2000年麻省理工学院出版社出版的《基于知识的神经计算》一书。他也是神经网络、计算智能和数据分析领域270多篇期刊和会议论文的作者或合著者。Zurada博士因在研究和教学方面的杰出表现而获得了许多奖项,包括1993年的研究、奖学金和创造性活动总统奖。1998年至2003年,Zurada博士担任IEEE Transactions on Neural Networks的主编。2004- 2005年,他担任IEEE计算智能协会主席。他是IEEE Fellow和NNS Distinguished Speaker。
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