Symbolic Approximate Reasoning Within Unbalanced Multi-sets: Application to Autism Diagnosis

International audienceIn most daily activities, humans often use imprecise information derived from appreciation instead of exact measurements to make decisions. Multisets allow the representation of imperfect information in a Knowledge-Based System (KBS), in the multivalued logic context. New facts are deduced using approximate reasoning. In the literature, dealing with imperfect information relies on an implicit assumption: the distribution of terms is uniform on a scale ranging from 0 to 1. Nevertheless, in some cases, a sub-domain of this scale may be more informative and may include more terms. In this work, we focus on approximate reasoning within these sets, known as unbalanced sets, in the context of multi-valued logic. We introduce an approach based on the Generalized Modus Ponens (GMP) model using Generalized Symbolic Modifiers (GSM). The proposed model is implemented in a tool for autism diagnosis by means of unbalanced severity degrees of the Childhood Autism Rating Scale (CARS). We obtain satisfying results on the distinction between autistic and not autistic child compared to psychiatrists diagnosis

Attributes regrouping in Fuzzy Rule Based Classification Systems: an intra-classes approach

International audienceFuzzy rule-based classification systems (FRBCS) are able to build linguistic interpretable models, they automatically generate fuzzy if-then rules and use them to classify new observations. However, in these supervised learning systems, a high number of predictive attributes leads to an exponential increase of the number of generated rules. Moreover the antecedent conditions of the obtained rules are very large since they contain all the attributes that describe the examples. Therefore the accuracy of these systems as well as their interpretability degraded. To address this problem, we propose to use ensemble methods for FRBCS where the decisions of different classifiers are combined in order to form the final classification model. We are interested in particular in ensemble methods which split the attributes into subgroups and treat each subgroup separately. We propose to regroup attributes by correlation search among the training set elements that belongs to the same class, such an intra-classes correlation search allows to characterize each class separately. Several experiences were carried out on various data. The results show a reduction in the number of rules and of antecedents without altering accuracy, on the contrary classification rates are even improved

Knowledge Based Supervised Classification: an Application to Image Processing

International audienceWe present a knowledge based supervised classification method. Our modelisation is based on automatic generation of classification rules. The classification function is directly given in the form of production rules base. The proposed learning method is multi-features, it allows to take into account the possible predictive power of a simultaneously considered features conjunction. On the other hand, the feature space partition allows a multi-valued representation of the features and data imprecision integration. The rules conclusions are accompanied by belief degrees. This uncertainty is managed in the learning phase as well as in the recognition one. To introduce more flexibility and overcome the boundary problem due to the discretisation, we propose to use approximate reasoning. We introduce, in this purpose, an adequate distance to compare neighboring facts. This distance, measuring imprecision, combined with uncertainty of classification decisions represented by belief degrees, drives the approximate inference. The proposed method was implemented in a tool called SUCRAGE and confronted with a real application in the field of image processing. The obtained results are very satisfactory. They validate our approach and allow us to consider other application fields

Induction supervisée d'images de règles: le système SUCRAGE

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Distributed collaborative reasoning for HAR in smart homes

International audienceDistributed Human Activity Recognition (D-HAR) is an active research issue for pervasive computing that aims to identify human activities in smart homes. This paper proposes a fully distributed multi-agent reasoning approach where agents, with diverse classifiers, observe sensor data, make local predictions and collaborate to identify current activities. Experimental tests on Aruba dataset indicate an enhancement in terms of accuracy and F-measure metrics compared either to a centralized approach or a distributed approach from the literatur

DCR: a new distributed model for human activity recognition in smart homes

International audienceHuman Activity Recognition (HAR) is an important research issue for pervasive computing that aims to identify human activities in smart homes. In the literature, most reasoning approaches for HAR are based on centralized approach where a central system is responsible for processing and reasoning about sensor data in order to recognize activities. Since sensor data are distributed, heterogeneous, and dynamic (i.e., whose characteristics are varying over time) in the smart home, reasoning process on these data for HAR needs to be distributed over a group of heterogeneous, autonomous and interacting entities in order to be more efficient. This paper proposes a main contribution, the DCR approach, a fully Distributed Collaborative Reasoning multi-agent approach where agents, with diverse classifiers, observe sensor data, make local predictions, communicate and collaborate to identify current activities. Then, an improved version of the DCR approach is proposed, the DCR-OL approach, a distributed Online Learning approach where learning agents learns from their collaborations to improve their own performance in activity recognition. Finally, we test our approaches by performing an evaluation study on Aruba dataset, that indicates an enhancement in terms of accuracy, F-measure and G-mean metrics compared to the centralized approach and also compared to a distributed approach existing in the literature

Using Fuzzy Modifiers in Colorimetry

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Comparison of fuzzy subsets: towards a linguistic approach

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Colorimetric Alterations by way of Linguistic Modifiers: A Fuzzy Approach vs. A Symbolic Approach

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A symbolic Approach for Colorimetric Alterations

International audienceTo modify colours, many solutions are possible. Here, we present an approach that uses symbolic linguistic modifiers. Colours are modified thanks to two kinds of words: a modifier and a qualifier. The first one is translated into a symbolic linguistic modifier whereas the second one into one or many fuzzy subsets. In fact, we can say that we associate words with these two kinds of simple mathematical object