Abstract book (pdf) - ICPR 2010

In this paper, we address the problem of active query selection for clustering with constraints. The objective is to determine
automatically a set of queries and their associated must-link and can-not link constraints to help constraints based clustering
algorithms to converge. Some works on active constraints learning have already been proposed but they are only applied
to K-Means like clustering algorithms which are known to be limited to spherical clusters while we are interested in constraints-based
clustering algorithms that deals with clusters of arbitrary shapes and sizes (like Constrained-DBSCAN,
Constrained-Hierarchical Clustering. . . ). Our novel approach relies on a k-nearest neighbors graph to estimate the dense
regions of the data space and generates queries at the frontier between clusters where the cluster membership is most uncertain.
Experiments show that our framework improves the performance of constraints based clustering algorithms.
13:30-16:30, Paper WeBCT8.24
Fuzzy Support Vector Machines for ECG Arrhythmia Detection
Özcan, N. Özlem, Boğaziçi Univ.
Gürgen, Fikret, Boğaziçi Univ.
Besides cardiovascular diseases, heart attacks are the main cause of death around the world. Pre-monitoring or pre-diagnostic
helps to prevent heart attacks and strokes. ECG plays a key role in this regard. In recent studies, SVM with different
kernel functions and parameter values are applied for classification on ECG data. The classification model of SVM can
be improved by assigning membership values for inputs. SVM combined with fuzzy theory, FSVM, is exercised on UCI
Arrhythmia Database. Five different membership functions are defined. It is shown that the accuracy of classification can
be improved by defining appropriate membership functions. ANFIS is used in order to interpret the resulting classification
model. The ANFIS model of the ECG data is compared to and found consistent with the medical knowledge.
13:30-16:30, Paper WeBCT8.25
ROC Analysis and Cost-Sensitive Optimization for Hierarchical Classifiers
Paclik, Pavel, PR Sys Design
Lai, Carmen, TU Delft
Landgrebe, Thomas, De Beers
Duin, Robert, TU Delft
Instead of solving complex pattern recognition problems using a single complicated classifier, it is often beneficial to
leverage our prior knowledge and decompose the problem into parts. These may be tackled using specific feature subsets
and simpler classifiers resulting in a hierarchical system. In this paper, we propose an efficient and scalable approach for
cost-sensitive optimization of a general hierarchical classifier using ROC analysis. This allows the designer to view the
hierarchy of trained classifiers as a system, and tune it according to the application needs.
13:30-16:30, Paper WeBCT8.26
Variational Mixture of Experts for Classification with Applications to Landmine Detection
Yuksel, Seniha Esen, Univ. of Florida
Gader, Paul, Univ. of Florida
In this paper, we (1) provide a complete framework for classification using Variational Mixture of Experts (VME); (2) derive
the variational lower bound; and (3) apply the method to landmine, or simply mine, detection and compare the results
to the Mixtures of Experts trained with Expectation Maximization (EMME). VME has previously been used for regression
and Waterhouse explained how to apply VME to classification (which we will call as VMEC). However, the steps to train
the model were not made clear since the equations were applicable to vector valued parameters as opposed to matrices for
each expert. Also, a variational lower bound was not provided. The variational lower bound provides an excellent stopping
criterion that resists over-training. We demonstrate the efficacy of the method on real-world mine classification; in which,
training robust mine classification algorithms is difficult because of the small number of samples per class. In our experiments
VMEC consistently improved performance over EMME.
13:30-16:30, Paper WeBCT8.27
A Unifying Framework for Learning the Linear Combiners for Classifier Ensembles
Erdogan, Hakan, Sabanci Univ.
Sen, Mehmet Umut, Sabanci Univ.
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