Rough Set Classifier Based on DSmT

International audienceThe classifier based on rough sets is widely used in pattern recognition. However, in the implementation of rough set-based classifiers, there always exist the problems of uncertainty. Generally, information decision table in Rough Set Theory (RST) always contains many attributes, and the classification performance of each attribute is different. It is necessary to determine which attribute needs to be used according to the specific problem. In RST, such problem is regarded as attribute reduction problems which aims to select proper candidates. Therefore, the uncertainty problem occurs for the classification caused by the choice of attributes. In addition, the voting strategy is usually adopted to determine the category of target concept in the final decision making. However, some classes of targets cannot be determined when multiple categories cannot be easily distinguished (for example, the number of votes of different classes is the same). Thus, the uncertainty occurs for the classification caused by the choice of classes. In this paper, we use the theory of belief functions to solve two above mentioned uncertainties in rough set classification and rough set classifier based on Dezert-Smarandache Theory (DSmT) is proposed. It can be experimentally verified that our proposed approach can deal efficiently with the uncertainty in rough set classifiers

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

Combinaison d’informations ponctuelles et volumiques pour le diagnostic d’ouvrages en terre soumis à des risques hydrauliques

In France, the river protection levees extend over more than 8,600 km. These structures are very heterogeneous, due to their construction and their history (breaks and repairs, extensions ...). Breaks of work are likely to lead to disastrous consequences such as loss of lives, economic and environmental disasters. In order to prevent the risk of breakage, special supervision of the protection levees is required. This surveillance begins with a visual recognition of the object of study as well as historical and bibliographic research. Recognized methodologies for the assessment of hydraulic structures including complementary geotechnical and geophysical reconnaissance methods are also being used. This work presents a new way of combining data from these two types of information sources, taking into account the specificities of each kind of method (level of imperfection associated with the data, spatial distribution of the information). This new methodology considers the framework fixed by the theory of belief masses and improves the characterization of lithological sets within levees by providing information on the level of conflict between information sources while proposing a confidence index associated with the results. The methodology is implemented through examples of subsoil sections characterized by synthetic and experimental data as well as by data from a real earthen levee.En France, les digues de protection fluviales s'étendent sur plus de 8 600 km. Ces ouvrages sont très hétérogènes, de par leur mode de construction et leur historique (ruptures et réparations, rehausses…). Lesruptures d'ouvrage sont susceptibles de mener à des conséquences désastreuses telles que des pertes humaines et économiques. Afin de prévenir les risques de rupture, une bonne gestion des digues de protection est requise. Elle inclut un diagnostic débutant par une reconnaissance visuelle de l'objet d'étude et par des recherches historiques et bibliographiques. Des méthodologies reconnues pour l'évaluation des ouvrages hydrauliques s’appuyant sur des méthodes complémentaires de reconnaissance géotechniques et géophysiques sont aussi employées. Ce travail présente une méthodologie de combinaisons d’informations, issues de ces deux familles de méthodes de reconnaissance, tenant compte des particularités de chaque méthode (niveau d'imperfection associé aux données, répartition spatiale de l’information). Cette nouvelle méthodologie considère le cadre fixé par la théorie des masses de croyance et améliore la caractérisation des ensembles lithologiques au sein des digues en renseignant sur le niveau de conflit entre les sources d'information tout en proposant un indice de confiance associé aux résultats. La méthodologie est mise en œuvre à travers des exemples de sections de sous-sols caractérisées à travers de données synthétiques et expérimentales ainsi qu'issues d'un véritable ouvrage hydraulique en terre