Abstract
In this paper we present an exemplary classifier (classification algorithm) based on two-layered learning. In the first layer of learning a collection of classifiers is induced from a part of original training data set. In the second layer classifiers are induced using patterns extracted from already constructed classifiers on the basis of their performance on the remaining part of training data. We report results of experiments performed on the following data sets, well known from literature: diabetes, heart disease, australian credit (see [5]) and lymphography (see [4]). We compare the standard rough set method used to induce classifiers (see [1] for more details), based on minimal consistent decision rules (see [6]), with the classifier based on two-layered learning.
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Bazan, J.G. (2004). Classifiers Based on Two-Layered Learning. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_42
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DOI: https://doi.org/10.1007/978-3-540-25929-9_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
Online ISBN: 978-3-540-25929-9
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