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IEEE Transactions on Evolutionary Computation

Issue 6 • Date Dec. 2003

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Displaying Results 1 - 8 of 8
  • Guest editorial data mining and knowledge discovery with evolutionary algorithms

    Publication Year: 2003, Page(s):517 - 518
    Cited by:  Papers (3)
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  • Acknowledgment to reviewers

    Publication Year: 2003, Page(s):576 - 578
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  • Author index

    Publication Year: 2003, Page(s):579 - 580
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  • Subject index

    Publication Year: 2003, Page(s):580 - 583
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  • A novel evolutionary data mining algorithm with applications to churn prediction

    Publication Year: 2003, Page(s):532 - 545
    Cited by:  Papers (71)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (893 KB) | HTML iconHTML

    Classification is an important topic in data mining research. Given a set of data records, each of which belongs to one of a number of predefined classes, the classification problem is concerned with the discovery of classification rules that can allow records with unknown class membership to be correctly classified. Many algorithms have been developed to mine large data sets for classification mo... View full abstract»

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  • Evolving accurate and compact classification rules with gene expression programming

    Publication Year: 2003, Page(s):519 - 531
    Cited by:  Papers (74)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (575 KB) | HTML iconHTML

    Classification is one of the fundamental tasks of data mining. Most rule induction and decision tree algorithms perform a local, greedy search to generate classification rules that are often more complex than necessary. Evolutionary algorithms for pattern classification have recently received increased attention because they can perform global searches. In this paper, we propose a new approach for... View full abstract»

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  • Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study

    Publication Year: 2003, Page(s):561 - 575
    Cited by:  Papers (85)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (905 KB) | HTML iconHTML

    Evolutionary algorithms are adaptive methods based on natural evolution that may be used for search and optimization. As data reduction in knowledge discovery in databases (KDDs) can be viewed as a search problem, it could be solved using evolutionary algorithms (EAs). In this paper, we have carried out an empirical study of the performance of four representative EA models in which we have taken i... View full abstract»

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  • A semantically guided and domain-independent evolutionary model for knowledge discovery from texts

    Publication Year: 2003, Page(s):546 - 560
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (833 KB) | HTML iconHTML

    We present a novel evolutionary model for knowledge discovery from texts (KDTs), which deals with issues concerning shallow text representation and processing for mining purposes in an integrated way. Its aims is to look for novel and interesting explanatory knowledge across text documents. The approach uses natural language technology and genetic algorithms to produce explanatory novel hypotheses... View full abstract»

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Aims & Scope

IEEE Transactions on Evolutionary Computation publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.
 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief

Dr. Kay Chen Tan (IEEE Fellow)

Department of Electrical and Computer Engineering

National University of Singapore

Singapore 117583

Email: eletankc@nus.edu.sg

Website: http://vlab.ee.nus.edu.sg/~kctan