Abstract
The quality of classification can be increased by using some feature extraction algorithm, i.e. the algorithm that finds new and more relevant features, before application of learning procedure. In this paper, we investigate a novel feature extraction method for textual data. Usually, texts (documents) are represented as collections of words or keywords. We present a method for finding new numerical attributes that improve the quality of classification. New features are based on a set of words (text pattern) and are defined as number of words occurring in both text pattern and the considered document. Our approach is based on Rough set methods and Lattice Machine theory. The experimental results show that the presented methods improve the classification quality on almost all textual data.
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Nguyen, H.S., Wang, H. (2002). Pattern Extraction Method for Text Classification. In: Bouchon-Meunier, B., Gutiérrez-RÃos, J., Magdalena, L., Yager, R.R. (eds) Technologies for Constructing Intelligent Systems 1. Studies in Fuzziness and Soft Computing, vol 89. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1797-3_18
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DOI: https://doi.org/10.1007/978-3-7908-1797-3_18
Publisher Name: Physica, Heidelberg
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