Younès Bennani

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Clustering is a very powerful tool for automatic detection of relevant sub-groups in unlabeled data sets. In this paper we focus on interval data: i.e. where the objects are defined as hyper-rectangles. We propose here a new clustering algorithm for interval data, based on the learning of a Self Organizing Map. The major advantage of our approach is that(More)
This paper presents a new approach called dendogram based support vector machines (DSVM), to treat multi-class problems. First, the method consists to build a taxonomy of classes in an ascendant manner done by ascendant hierarchical clustering method (AHC). Second, SVM is injected at each internal node of the taxonomy in order to separate the two subsets of(More)
The use of the wireless sensor networks (WSNs) should be increasing in different fields (scientist, logistic, military and health, etc.). However, the sensor’s size is an important limitation in term of energetic autonomy, and thus of lifetime because battery must be very small. This is the reason why, today, research mainly carries on the energy management(More)
We introduce a new learning approach, which provides simultaneously Self-Organizing Map (SOM) and local weight vector for each cluster. The proposed approach is computationally simple, and learns a different features vector weights for each cell (relevance vector). Based on the Self-Organizing Map approach, we present two new simultaneously clustering and(More)
This paper presents and evaluates a modular/hybrid connectionist system for speaker identification. Modularity has emerged as a powerful technique for reducing the complexity of connectionist systems, and allowing a priori knowledge to be incorporated into their design. Text-independent speaker identification is an inherently complex task where the amount(More)
Simultaneous selection of the number of clusters and of a relevant subset of features is part of data mining challenges. A new approach is proposed to address this difficult issue. It takes benefits of both two-levels clustering approaches and wrapper features selection algorithms. On the one hands, the former enhances the robustness to outliers and to(More)
In this paper, we propose a features selection measure and an architecture optimization procedure for Multi-Layer Perceptrons (MLP). The algorithm presented in this contribution employs a heuristic measure named HVS (Heuristic for Variable Selection). This new measure allows us to identify and select important variables in the features space. This can be(More)