Feature extraction for classification in the data mining process

M. Pechenizkiy, S. Puuronen, A. Tsymbal

    Research output: Contribution to journalArticleAcademicpeer-review

    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". Three different eigenvector-based feature extraction approaches are discussed and three different kinds of applications with respect to classification tasks are considered. The summary of obtained results concerning the accuracy of classification schemes is presented with the conclusion about the search for the most appropriate feature extraction method. The problem how to discover knowledge needed to integrate the feature extraction and classification processes is stated. 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
    Pages (from-to)271-278
    JournalInternational Journal on Information Theories and Applications
    Volume10
    Issue number1
    Publication statusPublished - 2003

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