Real-valued feature selection for process approximation and prediction

The selection of features for classification, clustering and approximation is an important task in pattern recognition, data mining and soft computing. For real-valued features, this contribution shows how feature select
The selection of features for classification, clustering and approximation is an important task in pattern recognition, data mining and soft computing. For real-valued features, this contribution shows how feature selection for a high number of features can be implemented using mutual in-formation. Especially, the common problem for mutual information computation of computing joint probabilities for many dimensions using only a few samples is treated by using the Rènyi mutual information of order two as computational base. For this, the Grassberger-Takens corre-lation integral is used which was developed for estimating probability densities in chaos theory. Additionally, an adaptive procedure for computing the hypercube size is introduced and for real world applications, the treatment of missing values is included. The computation procedure is accelerated by exploiting the ranking of the set of real feature values especially for the example of time series. As example, a small blackbox-glassbox example shows how the relevant features and their time lags are determined in the time series even if the input feature time series determine nonlinearly the output. A more realistic example from chemical industry shows that this enables a better ap-proximation of the input-output mapping than the best neural network approach developed for an international contest. By the computationally efficient implementation, mutual information becomes an attractive tool for feature selection even for a high number of real-valued features. Key words: feature selection, process approximation, Rènyi mutual information, classification, clustering, Takens-Grassberger correlation integral
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Metadaten
Author:Frank Heister, Rüdiger Brause
URN:urn:nbn:de:hebis:30-71795
ISSN:1868-8330
Series (Serial Number):Frankfurter Informatik-Berichte (09, 01)
Publisher:Goethe-Univ., Fachbereich Informatik und Mathematik, Inst. für Informatik
Place of publication:Frankfurt am Main
Document Type:Working Paper
Language:English
Year of Completion:2009
Year of first Publication:2009
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2010/09/08
HeBIS PPN:226762114
Institutes:Informatik
Dewey Decimal Classification:004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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