Abstract book (pdf) - ICPR 2010

11:40-12:00, Paper MoAT4.3
A Score Decidability Index for Dynamic Score Combination
Lobrano, Carlo, DIEE- Univ. of Cagliari
Tronci, Roberto, Univ. of Cagliari
Giacinto, Giorgio, Univ. of Cagliari
Roli, Fabio, Univ. of Cagliari
In two-class problems, the combination of the outputs (scores) of an ensemble of classifiers is widely used to attain high
performance. Dynamic combination techniques that estimate the combination parameters on a pattern per pattern basis,
usually provide better performance than those of static combination techniques. In this paper, we propose an Index of Decidability
derived from the Wilcox on-Mann-Whitney statistic, that is used to estimate the combination parameters. Reported
results on a multimodal biometric dataset show the effectiveness of the proposed dynamic combination mechanisms
in terms of misclassification errors.
12:00-12:20, Paper MoAT4.4
AUC-Based Combination of Dichotomizers: Is Whole Maximization also Effective for Partial Maximization?
Ricamato, Maria Teresa, Univ. degli Studi di Cassino
Tortorella, Francesco, Univ. degli Studi di Cassino
The combination of classifiers is an established technique to improve the classification performance. When dealing with
two-class classification problems, a frequently used performance measure is the Area under the ROC curve (AUC) since
it is more effective than accuracy. However, in many applications, like medical or biometric ones, tests with false positive
rate over a given value are of no practical use and thus irrelevant for evaluating the performance of the system. In these
cases, the performance should be measured by looking only at the interesting part of the ROC curve. Consequently, the
optimization goal is to maximize only a part of the AUC instead of the whole area. In this paper we propose a method tailored
for these situations which builds a linear combination of two dichotomizers maximizing the partial AUC (pAUC).
Another aim of the paper is to understand if methods that maximize the AUC can maximize also the pAUC. An empirical
comparison drawn between algorithms maximizing the AUC and the proposed method shows that this latter is more effective
for the pAUC maximization than methods designed to globally optimize the AUC.
12:20-12:40, Paper MoAT4.5
Random Prototypes-Based Oracle for Selection-Fusion Ensembles
Armano, Giuliano, Univ. of Cagliari
Hatami, Nima, Univ. of Cagliari
Classifier ensembles based on selection-fusion strategy have recently aroused enormous interest. The main idea underlying
this strategy is to use miniensembles instead of monolithic base classifiers in an ensemble in order to improve the overall
performance. This paper proposes a classifier selection method to be used in selection-fusion strategies. The method involves
first splitting the original classification problem according to some prototypes randomly selected from training
data, and then building a classifier on each subset. The trained classifiers, together with an oracle used to switch between
them, form a miniensemble of classifier selection. With respect to the other methods used in the selection-fusion framework,
the proposed method has proven to be more efficient in the decomposition process with no limitation in the number of resulting
partitions. Experimental results on some datasets from the UCI repository show the validity of the proposed method.
MoAT5 Dolmabahçe Hall B
Detection and Segmentation of Audio Signals Regular Session
Session chair: Erdogan, Hakan (Sabanci Univ.)
11:00-11:20, Paper MoAT5.1
Noise-Robust Voice Activity Detector based on Hidden Semi-Markov Models
Liu, Xianglong, Beihang Univ.
Liang, Yuan, Beihang Univ.
Lou, Yihua, Beihang Univ.
Li, He, Beihang Univ.
Shan, Baosong, Beihang Univ.
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