Feature extraction for classification in knowledge discovery systems

M. Pechenizkiy, S. Puuronen, A. Tsymbal

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    9 Citations (Scopus)

    Abstract

    Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of "the curse of dimensionality". We consider three different eigenvector-based feature extraction approaches for classification. The summary of obtained results concerning the accuracy of classification schemes is presented and the issue of search for the most appropriate feature extraction method for a given data set is considered. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up the proposed system are considered.
    Original languageEnglish
    Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems (Proceedings 7th International Conference, KES 2003, Oxford, UK, September 3-5, 2003), Part I
    EditorsV. Palade, R.J. Howlett, L.C. Jain
    Place of PublicationBerlin
    PublisherSpringer
    Pages526-532
    ISBN (Print)3-540-40803-7
    DOIs
    Publication statusPublished - 2003

    Publication series

    NameLecture Notes in Computer Science
    Volume2773
    ISSN (Print)0302-9743

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