Automatic video annotation with forests of fuzzy decision trees

Nowadays, the annotation of videos with high-level semantic concepts or features is a great challenge. In this paper, this problem is tackled by learning, by means of Fuzzy Decision Trees (FDT), automatic rules based on a limited set of examples. Rules intended, in an exploitation step, to reduce the need of human usage in the process of indexation. However, when addressing large, unbalanced, multiclass example sets, a single classi er - such as the FDT - is insu cient. Therefore we introduce the use of forests of fuzzy decision trees (FFDT) and we highlight: (a) its e ectiveness on a high level feature detection task, compared to other competitive systems and (b) the e ect on performance from the number of classi ers point of view. Moreover, since the resulting indexes are, by their nature, to be used in a retrieval application, we discuss the results under the lights of a ranking (vs. a classi cation) context.Peer Reviewe

Sparsity-Inducing Fuzzy Subspace Clustering

This paper considers a fuzzy subspace clustering problem and proposes to introduce an original sparsity-inducing regularization term. The minimization of this term, which involves a l$_{0}$ penalty, is considered from a geometric point of view and a novel proximal operator is derived. A subspace clustering algorithm, Prosecco, is proposed to optimize the cost function using both proximal and alternate gradient descent. Experiments comparing this algorithm to the state of the art in sparse fuzzy subspace clustering show the relevance of the proposed approach

OSACA: Découverte d'attributs symboliques ordinaux

International audienceThis paper proposes to exploit heterogeneous data, i.e. data described by both numerical and categorical features, so as to discover whether, based on information provided by the numerical attributes, some categorical attributes actually are ordinal ones. The proposed 3-step methodology OSACA, first extracts gradual patterns from the numerical attributes ; it then applies mathematical morphology tools to induce an associated order on the categorical attributes. The third step evaluates the quality of the candidate rankings through measures derived from the rank entropy discrimination.Les bases de données dites hétérogènes contiennent des données décrites par des attributsà la fois symboliques et numériques. Cet article propose une méthode, appelée OSACA, pour identifier, parmi les attributs symboliques, les attributs ordinaux, en exploitant les informations fournies par les attributs numériques. Pour ce faire, OSACA procède en trois étapes : des motifs graduels sont d'abord extraits des attributs numériques. Des filtres morphologiques sont ensuite appliqués aux attributs symboliques pour déterminer des ordres sur les valeurs catégoriellesà partir de l'ordre induit par les motifs graduels. Enfin, une mesure d'entropie d'ordre per-met d'évaluer la pertinence des ordres candidats

Twelve numerical, symbolic and hybrid supervised classification methods

International audienceSupervised classification has already been the subject of numerous studies in the fields of Statistics, Pattern Recognition and Artificial Intelligence under various appellations which include discriminant analysis, discrimination and concept learning. Many practical applications relating to this field have been developed. New methods have appeared in recent years, due to developments concerning Neural Networks and Machine Learning. These "hybrid" approaches share one common factor in that they combine symbolic and numerical aspects. The former are characterized by the representation of knowledge, the latter by the introduction of frequencies and probabilistic criteria. In the present study, we shall present a certain number of hybrid methods, conceived (or improved) by members of the SYMENU research group. These methods issue mainly from Machine Learning and from research on Classification Trees done in Statistics, and they may also be qualified as "rule-based". They shall be compared with other more classical approaches. This comparison will be based on a detailed description of each of the twelve methods envisaged, and on the results obtained concerning the "Waveform Recognition Problem" proposed by Breiman et al which is difficult for rule based approaches

Application of Fuzzy Rule Induction to Data Mining

In this paper, a data mining process to induce a set of fuzzy rules from a database is presented. This process is based on the construction of fuzzy decision trees. Wc present a method to construct fuzzy decision trees and a method to use them to classify new examples. In presence of databases, prerequisites for training sets arc introduced to generate a good subset of data that will enable us to construct a fuzzy decision tree. Moreover, wc present different kinds of rules that can bc induced by means of the construction of a decision tree, and wc discuss some possible uses of such knowledge

Fuzzy Decision Trees to Help Flexible Querying

this paper, a study is done that enables us to show that classification by means of a fuzzy decision tree is equivalent to the generalized modus ponens. Moreover, it is shown that the decision taken by means of a fuzzy decision trcc is more stable when observation evolve

Incremental Tuning of Fuzzy Decision Trees

International audienceHandling stream data or temporal data is a difficult task and brings out a lot of problems to classical learning algorithms as the decision tree construction algorithms. In that context, incremental algorithms have been proposed but they often lie on the frequent reconstruction of the decision tree when this one provides a high number of misclassified examples. In this paper, we proposed a new algorithm to incrementally tune a fuzzy decision tree (FDT) that limit the number of reconstructions of the tree. That algorithm takes benefit of the fuzzy classification provided by a FDT to introduce a local tuning of the internal nodes of the FDT and avoid a complete reconstruction of the tree

Construction d'arbres de décision flous : le système Salammbô

National audienc