Grégory Smits

Bio: Grégory Smits is an academic researcher from University of Rennes. The author has contributed to research in topics: Fuzzy logic & Tuple. The author has an hindex of 9, co-authored 69 publications receiving 290 citations.

On a Fuzzy Algebra for Querying Graph Databases

Abstract: This paper proposes a notion of fuzzy graph database and describes a fuzzy query algebra that makes it possible to handle such database, which may be fuzzy or not, in a flexible way. The algebra, based on fuzzy set theory and the concept of a fuzzy graph, is composed of a set of operators that can be used to express preference queries on fuzzy graph databases. The preferences concern i) the content of the vertices of the graph and ii) the structure of the graph. In a similar way as relational algebra constitutes the basis of SQL, the fuzzy algebra proposed here underlies a user-oriented query language and an associated tool implementing this language that are also presented in the paper.

ReqFlex: fuzzy queries for everyone

Abstract: In this demonstration we present a complete fuzzy-set-based approach to preference queries that tackles the two main questions raised by the introduction of flexibility and personalization when querying relational databases: i) how to efficiently execute preference queries? and, ii) how to help users define preferences and queries? As an answer to the first question, we propose PostgreSQL_f, a module implemented on top of PostgreSQL to handle fuzzy queries. To answer the second question, we propose ReqFlex an intuitive user interface to the definition of preferences and the construction of fuzzy queries.

Efficient Generation of Reliable Estimated Linguistic Summaries

Abstract: Summarizing data with linguistic statements is a crucial and topical issue that has been largely addressed by the soft computing community. The goal of summarization is to generate statements that linguistically describe the properties observed in a dataset. This paper addresses the issue of efficiently extracting these summaries and rendering them to the final user, in the case where the data to be summarized are stored in a relational data base: it proposes a novel strategy that leverages the statistics about the data distribution maintained by the database system. This paper shows that reliable summaries can be very efficiently estimated based on these statistics only and without any costly data access. Additionally, it proposes a visualization of the set of extracted summaries that offers a fruitful interactive exploration tool to the user. Experiments performed on two real data bases show the relevance and efficiency of the proposed approach: with a negligible loss of accuracy, we provide the first linguistic summarization approach whose processing time does not depend on the size of the dataset. The generation of estimated linguistic summaries takes less than one second even for dataset containing millions of tuples.

A Distance-Based Decision in the Credal Level

Abstract: Belief function theory provides a flexible way to combine information provided by different sources. This combination is usually followed by a decision making which can be handled by a range of decision rules. Some rules help to choose the most likely hypothesis. Others allow that a decision is made on a set of hypotheses. In [6], we proposed a decision rule based on a distance measure. First, in this paper, we aim to demonstrate that our proposed decision rule is a particular case of the rule proposed in [4]. Second, we give experiments showing that our rule is able to decide on a set of hypotheses. Some experiments are handled on a set of mass functions generated randomly, others on real databases.

Adequacy of a user-defined vocabulary to the data structure

Abstract: Clustering methods are of a particular interest to discover and to summarize the structure of a data set. However, interpreting clusters may be abstruse for unexperienced users who most of the time possess their own vocabulary to describe data and properties. In this article, an approach is proposed to determine and quantify how appropriate a user-defined vocabulary is regarding the structure captured on the data distribution using a clustering method. Two measures of vocabulary appropriateness based on clustering are proposed and tested on artificial data.

Combination of Classifiers With Optimal Weight Based on Evidential Reasoning

Abstract: In pattern classification problem, different classifiers learnt using different training data can provide more or less complementary knowledge, and the combination of classifiers is expected to improve the classification accuracy. Evidential reasoning (ER) provides an efficient framework to represent and combine the imprecise and uncertain informations. In this paper, we want to focus on the weighted combination of classifiers based on ER. Because each classifier may have different performance on the given dataset, the classifiers to combine are considered with different weights. A new weighted classifier combination method is proposed based on ER to enhance the classification accuracy. The optimal weighting factors of classifiers are obtained by minimizing the distances between fusion results obtained by Dempster's rule and the target output in training data space to fully take advantage of the complementarity of the classifiers. A confusion matrix is additionally introduced to characterize the probability of the object belonging to one class but classified to another class by the fusion result. This matrix is also optimized using training data jointly with classifier weight, and it is used to modify the fusion result to make it as close as possible to truth. Moreover, the training patterns are considered with different weights for the parameter optimization in classifier fusion, and the patterns hard to classify are committed with bigger weight than the ones easy to deal with. The pattern weight and the other parameters (i.e., classifier weight and confusion matrix) are iteratively optimized for obtaining the highest classification accuracy. A cautious decision making strategy is introduced to reduce the errors, and the pattern hard to classify will be cautiously committed to a set of classes, because the partial imprecision of decision is considered better than error in certain case. The effectiveness of the proposed method is demonstrated with various real datasets from UCI repository, and its performances are compared with those of other classical methods.

Information Processing and Management of Uncertainty in Knowledge-Based Systems

Abstract: These three volumes (CCIS 442, 443, 444) constitute the proceedings of the 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2014, held in Montpellier, France, July 15-19, 2014. The 180 revised full papers presented together with five invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on uncertainty and imprecision on the web of data; decision support and uncertainty management in agri-environment; fuzzy implications; clustering; fuzzy measures and integrals; non-classical logics; data analysis; real-world applications; aggregation; probabilistic networks; recommendation systems and social networks; fuzzy systems; fuzzy logic in boolean framework; management of uncertainty in social networks; from different to same, from imitation to analogy; soft computing and sensory analysis; database systems; fuzzy set theory; measurement and sensory information; aggregation; formal methods for vagueness and uncertainty in a many-valued realm; graduality; preferences; uncertainty management in machine learning; philosophy and history of soft computing; soft computing and sensory analysis; similarity analysis; fuzzy logic, formal concept analysis and rough set; intelligent databases and information systems; theory of evidence; aggregation functions; big data - the role of fuzzy methods; imprecise probabilities: from foundations to applications; multinomial logistic regression on Markov chains for crop rotation modelling; intelligent measurement and control for nonlinear systems.