Publikation: Discriminative Power of Input Features in a Fuzzy Model
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In many modern data analysis scenarios the first and most urgent task consists of reducing the redundancy in high dimensional input spaces. A method is presented that quantifies the discriminative power of the input features in a fuzzy model. A possibilistic information measure of the model is defined on the basis of the available fuzzy rules and the resulting possibilistic information gain, associated with the use of a given input dimension, characterizes the input feature’s discriminative power. Due to the low computational expenses derived from the use of a fuzzy model, the proposed possibilistic information gain generates a simple and efficient algorithm for the reduction of the input dimensionality, even for high dimensional cases. As real-world example, the most informative electrocardiographic measures are detected for an arrhythmia classification problem.
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SILIPO, Rosaria, Michael R. BERTHOLD, 1999. Discriminative Power of Input Features in a Fuzzy Model. In: HAND, David J., ed., Joost N. KOK, ed., Michael R. BERTHOLD, ed.. Advances in Intelligent Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999, pp. 87-98. Lecture Notes in Computer Science. 1642. ISBN 978-3-540-66332-4. Available under: doi: 10.1007/3-540-48412-4_8BibTex
@inproceedings{Silipo1999-07-08Discr-24076, year={1999}, doi={10.1007/3-540-48412-4_8}, title={Discriminative Power of Input Features in a Fuzzy Model}, number={1642}, isbn={978-3-540-66332-4}, publisher={Springer Berlin Heidelberg}, address={Berlin, Heidelberg}, series={Lecture Notes in Computer Science}, booktitle={Advances in Intelligent Data Analysis}, pages={87--98}, editor={Hand, David J. and Kok, Joost N. and Berthold, Michael R.}, author={Silipo, Rosaria and Berthold, Michael R.} }
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