Abstract-The possibility to face pattern recognition problems directly on structured domains (e.g., multimedia data, strings, graphs) is fundamental to the effective solution of many interesting applications. In this paper, we deal with a clustering problem defined in the string domain, focusing on the problem of cluster representation in data domains where only a dissimilarity measure can be fixed. To this aim, we adopt the MinSOD (Minimum Sum of Distances) cluster representation technique, which defines the representative as the element of the cluster minimizing the sum of dissimilarities from all the other elements in the considered set. Since the precise computation of the MinSOD have a high computational cost, we propose a suboptimal procedure consisting in computing the representative of the cluster considering only a reduced pool of samples, instead of the whole set of objects in the cluster. We have carried out some tests in order to ascertain the sensitivity of the clustering procedure with respect to the number of samples in the pool used to compute the MinSOD. Results show a good robustness of the proposed procedure. The implementations are available as part of the SPARE library, which is available as an open source project.
Recently there is a great interest in artificial systems able to understand and recognize human emotions. In this paper an Emotion Recognition System based on classical neural networks and neuro-fuzzy classifiers is proposed. Emotion recognition is performed in real time starting from a video stream acquired by a common webcam monitoring the user's face. Neurofuzzy classifiers, in comparison with Multi Layer Perceptron trained by EBP algorithm, show very short training times, allowing applications with easy and automated set up procedures, to be used in a wide range of applications, from entertainment to safety. The algorithm yields very interesting performances and can be adopted to recognize emotions as well as possible pathological conditions of the individual to be monitored
In this paper we present an innovative procedure for sequence mining and representation. It can be used as its own in Data Mining problems or as the core of a classification system based on a Granular Computing approach to represent sequences in a suited embedding space. By adopting an inexact sequence matching procedure, the algorithm is able to extract a symbols alphabet of frequent subsequences to be used as prototypes for the embedding stage. Experimental evaluation over both synthetically generated and biological datasets confirms that the modeling system is able to synthesize effective models when facing even complex and noisy problems defined by frequency-based classification rules
Many interesting applications of Pattern Recognition techniques can take advantage in dealing with labeled graphs as input patterns. To this aim, the most important issue is the definition of a dissimilarity measure between graphs. In this paper, we outline an ensemble of methods for dealing with such data,focusing on two specific methods. The first one is simply based on a global alignment approach applied to seriated versions of the graphs. The second one is a two-stages method, which applies a recurrent substructures analysis to the seriated graphs, individuating a set of frequent subsequences, employed for embedding the graphs into a real valued feature vector space. Tests have been performed by synthetically generating a set of classification problem instances with increasing problem hardness, and with a shared benchmarking database of labeled graphs
In this paper we propose an image classification system able to solve automatically a large set of problem instances by a granular computing approach. By means of a watershed segmentation algorithm, each image is decomposed into a set of segments (information granules), characterized by suited color, texture and shape features (segment signature). Successively, images are represented by a symbolic graph, where each node stores the segment signature and edges retain the information about the mutual spatial relations between segments. The induction engine is based on a parametric dissimilarity measure between graphs. A heuristic search procedure based on a genetic algorithm is able to find automatically both the segmentation parameters and the dissimilarity measure parameters, and hence the relevant features to the classification problem at hand. System performances have been measured on the basis of an image classification problem repository which has been specifically created to this aim
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