TY - UNPD
A1 - Heister, Frank
A1 - Brause, Rüdiger
T1 - Real-valued feature selection for process approximation and prediction
T2 - Frankfurter Informatik-Berichte ; Nr. 09,1
N2 - 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.
T3 - Frankfurter Informatik-Berichte - 09, 01
KW - feature selection
KW - process approximation
KW - Rènyi mutual information
KW - classification
KW - clustering
KW - Takens-Grassberger correlation integral
Y1 - 2009
UR - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7967
UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hebis:30-71795
SN - 1868-8330
PB - Goethe-Univ., Fachbereich Informatik und Mathematik, Inst. für Informatik
CY - Frankfurt am Main
ER -