In this paper initially we propose a novel approach to classify handwritten characters based on a directional decomposition of the corresponding chain-code representation. This is alternative to previous transformations of the chain-codes proposed by the authors, namely the ordered and random decomposition of the bit-planes resulting from the binary representation of the chain-codes. Subsequent...

Multiple classifiers have shown capability to improve performance in pattern recognition. This process can improve the overall accuracy of the system by using an optimal decision criteria. In this paper we propose an approach using a weighted benevolent fusion strategy to combine two state of the art pixel based motion classifiers. Tests on outdoor and indoor sequences confirm the efficacy of t...

In this paper we study the effectiveness of using multiple classifier combination for EEG signals classification aiming to obtain more accurate results than it possible from single classifier system. The developed system employs different features vectors fused at the abstract and measurement levels for integrating information to reach a collective decision. For making decision, the majority vo...

Land use and land cover (LULC) maps are remote sensing products that are used to classify areas into different landscapes. The newest techniques have been applied to improve the final LULC classification and most of them are based on SVM classifiers. In this paper, a new method based on a multiple classifiers ensemble to improve LULC map accuracy is shown. The method builds a statistical raster...

In the context of Multiple Classifier Systems, diversity among base classifiers is known to be a necessary condition for improvement in ensemble performance. In this paper the ability of several pair-wise diversity measures to predict generalisation error is compared. A new pair-wise measure, which is computed between pairs of patterns rather than pairs of classifiers, is also proposed for two-...

We present a multimodal open-set speaker identification system that integrates information coming from audio, face and lip motion modalities. For fusion of multiple modalities, we propose a new adaptive cascade rule that favors reliable modality combinations through a cascade of classifiers. The order of the classifiers in the cascade is adaptively determined based on the reliability of each mo...

In this paper, we present Information Market based Fusion (IMF), a novel, multi-classifier combiner method for decision fusion that is based on information markets. IMF does not require training or a static ensemble composition, adjusts to changes in base-classifier accuracy, provides incentives for the base-classifiers to present truthful information, and integrates with existing multi-agent s...

A multi-classifier system obtained by combining several individual classifiers usually exhibits a better performance (precision) than any of the original classifiers. In this work we use a multi-classifier based on a forest of randomly generated fuzzy decision trees (Fuzzy Random Forest), and we propose a new method to combine their decisions to obtain the final decision of the forest. The prop...