Paper Info
Title
Discriminative Feature Selection for Automatic Classification of Volcano-Seismic Signals.
Abstract
Feature extraction is a critical element in automatic pattern classification. In this letter, we propose different sets of parameters for classification of volcano-seismic signals, and the discriminative feature selection (DFS) method is applied for selecting the minimum number of features containing most of the discriminative information. We have applied DFS to a conventional cepstral-based parameterization (with 39 features) and to an extended set of parameters (including 84 features). Classification experiments using seismograms recorded at Colima Volcano (Mexico) show that, for the most complex classifier and using the cepstral-based parameterization, DFS provided a reduction of the error rate from 24.3% (using 39 features) to 15.5% (ten components). When DFS is applied to the extended parameterization, the error rate decreased from 27.9% (84 features) to 13.8% (14 features). These results show the utility of DFS for identifying the best components from the original feature vector and for exploring new parameterizations for the classification of volcano-seismic signals.
Year
DOI
Venue
2012
10.1109/LGRS.2011.2162815
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Keywords
Field
DocType
Cepstrum,cost function,discriminative feature selection (DFS),feature extraction,minimum classification error (MCE),pattern classification,seismic signal classification
Feature vector,Feature selection,Pattern recognition,Cepstrum,Word error rate,Feature extraction,Artificial intelligence,Classifier (linguistics),Hidden Markov model,Discriminative model,Mathematics
Journal
Volume
Issue
ISSN
9
2
1545-598X
Citations 
PageRank 
References 
3
0.47
0
Authors
5
Name
Order
Citations
PageRank
Isaac Álvarez1144.88
Luz García2639.48
Guillermo Cortés3244.32
Guillermo Benítez430.47
Ángel de la Torre548234.91