Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper ThBCT8.52
Combining Single Class Features for Improving Performance of a Two Stage Classifier
Cordella, Luigi P., Univ. di Napoli Federico II
De Stefano, Claudio, Univ. of Cassino
Fontanella, Francesco, Univ. of Cassino
Marrocco, Cristina, Univ. of Cassino
Scotto Di Freca, Alessandra, Univ. of Cassino
We propose a feature selection—based approach for improving classification performance of a two stage classification
system in contexts where a high number of features is involved. A problem with a set of N classes is subdivided into a set
of N two class problems. In each problem, a GA—based feature selection algorithm is used for finding the best subset of
features. These subsets are then used for training N classifiers. In the classification phase, unknown samples are given in
input to each of the trained classifiers by using the corresponding subspace. In case of conflicting responses, the sample
is sent to a suitably trained supplementary classifier. The proposed approach has been tested on a real world dataset containing
hyper—spectral image data. The results favourably compare with those obtained by other methods on the same
data.
13:30-16:30, Paper ThBCT8.53
The Rex Leopold II Model: Application of the Reduced Set Density Estimator to Human Categorization
De Schryver, Maarten, Ghent Univ.
Roelstraete, Bjorn, Ghent Univ.
Reduction techniques are important tools in machine learning and pattern recognition. In this article, we demonstrate how
a kernel-based density estimator can be used as a tool for understanding human category representation. Despite the dominance
of exemplar models of categorization, there is still ambiguity about the number of exemplars stored in memory.
Here, we illustrate that by omitting exemplars categorization performance is not affected.
13:30-16:30, Paper ThBCT8.54
A Hybrid Method for Feature Selection based on Mutual Information and Canonical Correlation Analysis
Sakar, Cemal Okan, Bahcesehir Univ.
Kursun, Olcay, Istanbul Univ.
Mutual Information (MI) is a classical and widely used dependence measure that generally can serve as a good feature selection
algorithm. However, under-sampled classes or rare but certain relations are overlooked by this measure, which can
result in missing relevant features that could be very predictive of variables of interest, such as certain phenotypes or disorders
in biomedical research, rare but dangerous factors in ecology, intrusions in network systems, etc. On the other hand,
Kernel Canonical Correlation Analysis (KCCA) is a nonlinear correlation measure effectively used to detect independence
but its use for feature selection or ranking is limited due to the fact that its formulation is not intended to measure the
amount of information (entropy) of the dependence. In this paper, we propose Predictive Mutual Information (PMI), a hybrid
measure of relevance not only is based on MI but also accounts for predictability of signals from one another as in
KCCA. We show that PMI has more improved feature detection capability than MI and KCCA, especially in catching
suspicious coincidences that are rare but potentially important not only for subsequent experimental studies but also for
building computational predictive models which is demonstrated on two toy datasets and a real intrusion detection system
dataset.
13:30-16:30, Paper ThBCT8.55
Speech Magnitude-Spectrum Information-Entropy (MSIE) for Automatic Speech Recognition in Noisy Environments
Nolazco-Flores, Juan A., Inst. Tecnológico y de Estudios Superiores de Monterrey
Aceves-López, Roberto A., Inst. Tecnológico y de Estudios Superiores de Monterrey
García-Perera, L. Paola, Inst. Tecnológico y de Estudios Superiores de Monterrey
The Magnitude-Spectrum Information-Entropy (MSIE) of the speech signal is presented as an alternative representation
of the speech that can be used to mitigate the mismatch between training and testing conditions. The speech-magnitude
spectrum is considered as a random variable from which entropy coefficients can be calculated for each frame. By concatenating
these entropic coefficients to its corresponding MFCC vector, then calculating the dynamic coefficients, and
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