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.
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© 2003 Springer-Verlag Berlin Heidelberg
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Pechenizkiy, M., Puuronen, S., Tsymbal, A. (2003). Feature Extraction for Classification in Knowledge Discovery Systems. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_72
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DOI: https://doi.org/10.1007/978-3-540-45224-9_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40803-1
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