Abstract
Artificial neural networks hold the established position of efficient classifiers used in decision support systems, yet to be efficient an ANN-based classifier requires careful selection of features. The excessive number of conditional attributes is not a guarantee of high classification accuracy, it means gathering and storing more data, and increasing the size of the network. Also the implementation of the trained network can become complex and the classification process takes more time. This line of reasoning leads to conclusion that the number of features should be reduced as far as possible without diminishing the power of the classifier. The paper presents investigations on attribute reduction process performed by exploiting the concept of reducts from the rough set theory and employed within stylometric analysis of literary texts that belongs with automatic categorisation tasks.
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Stańczyk, U. (2009). Relative Reduct-Based Selection of Features for ANN Classifier. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_35
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DOI: https://doi.org/10.1007/978-3-642-00563-3_35
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