107 research outputs found

    Evidential Clustering: A Review

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    International audienceIn evidential clustering, uncertainty about the assignment of objects to clusters is represented by Dempster-Shafer mass functions. The resulting clustering structure, called a credal partition, is shown to be more general than hard, fuzzy, possibilistic and rough partitions, which are recovered as special cases. Three algorithms to generate a credal partition are reviewed. Each of these algorithms is shown to implement a decision-directed clustering strategy. Their relative merits are discussed

    Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory

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    Land cover classification using multispectral satellite image is a very challenging task with numerous practical applications. We propose a multi-stage classifier that involves fuzzy rule extraction from the training data and then generation of a possibilistic label vector for each pixel using the fuzzy rule base. To exploit the spatial correlation of land cover types we propose four different information aggregation methods which use the possibilistic class label of a pixel and those of its eight spatial neighbors for making the final classification decision. Three of the aggregation methods use Dempster-Shafer theory of evidence while the remaining one is modeled after the fuzzy k-NN rule. The proposed methods are tested with two benchmark seven channel satellite images and the results are found to be quite satisfactory. They are also compared with a Markov random field (MRF) model-based contextual classification method and found to perform consistently better.Comment: 14 pages, 2 figure

    Naive possibilistic classifiers for imprecise or uncertain numerical data

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    International audienceIn real-world problems, input data may be pervaded with uncertainty. In this paper, we investigate the behavior of naive possibilistic classifiers, as a counterpart to naive Bayesian ones, for dealing with classification tasks in the presence of uncertainty. For this purpose, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty in data representation. Here the possibility distributions that are used are supposed to encode the family of Gaussian probabilistic distributions that are compatible with the considered dataset. We consider two types of uncertainty: (i) the uncertainty associated with the class in the training set, which is modeled by a possibility distribution over class labels, and (ii) the imprecision pervading attribute values in the testing set represented under the form of intervals for continuous data. Moreover, the approach takes into account the uncertainty about the estimation of the Gaussian distribution parameters due to the limited amount of data available. We first adapt the possibilistic classification model, previously proposed for the certain case, in order to accommodate the uncertainty about class labels. Then, we propose an algorithm based on the extension principle to deal with imprecise attribute values. The experiments reported show the interest of possibilistic classifiers for handling uncertainty in data. In particular, the probability-to-possibility transform-based classifier shows a robust behavior when dealing with imperfect data

    Evidential Bagging: Combining Heterogeneous Classifiers in the Belief Functions Framework

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    International audienceIn machine learning, Ensemble Learning methodologies are known to improve predictive accuracy and robustness. They consist in the learning of many classifiers that produce outputs which are finally combined according to different techniques. Bagging, or Bootstrap Aggre-gating, is one of the most famous Ensemble methodologies and is usually applied to the same classification base algorithm, i.e. the same type of classifier is learnt multiple times on bootstrapped versions of the initial learning dataset. In this paper, we propose a bagging methodology that involves different types of classifier. Classifiers' probabilist outputs are used to build mass functions which are further combined within the belief functions framework. Three different ways of building mass functions are proposed; preliminary experiments on benchmark datasets showing the relevancy of the approach are presented

    PROBABILITY DISTRIBUTION OVER THE SET OF CLASSES IN ARABIC DIALECT CLASSIFICATION TASK

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    Subject of Research.We propose an approach for solving machine learning classification problem that uses the information about the probability distribution on the training data class label set. The algorithm is illustrated on a complex natural language processing task - classification of Arabic dialects. Method. Each object in the training set is associated with a probability distribution over the class label set instead of a particular class label. The proposed approach solves the classification problem taking into account the probability distribution over the class label set to improve the quality of the built classifier. Main Results. The suggested approach is illustrated on the automatic Arabic dialects classification example. Mined from the Twitter social network, the analyzed data contain word-marks and belong to the following six Arabic dialects: Saudi, Levantine, Algerian, Egyptian, Iraq, Jordan, and to the modern standard Arabic (MSA). The paper results demonstrate an increase of the quality of the built classifier achieved by taking into account probability distributions over the set of classes. Experiments carried out show that even relatively naive accounting of the probability distributions improves the precision of the classifier from 44% to 67%. Practical Relevance. Our approach and corresponding algorithm could be effectively used in situations when a manual annotation process performed by experts is connected with significant financial and time resources, but it is possible to create a system of heuristic rules. The implementation of the proposed algorithm enables to decrease significantly the data preparation expenses without substantial losses in the precision of the classification

    Combination of Evidence in Dempster-Shafer Theory

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    Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

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    Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making
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