33 research outputs found
Problemes d'optimisation relatifs aux tableaux multiples .Point de vue de la classification automatique
Identification with iterative nearest neighbors using domain knowledge
A new iterative and interactive algorithm called CSN (Classification
by Successive Neighborhood) to be used in a complex descriptive objects
identification approach is presented. Complex objects are those designed
by experts within a knowledge base to describe taxa (monography species)
and also real organisms (collection specimens). The algorithm consists of
neighborhoods computations from an incremental basis of characters using
a dissimilarity function which takes into account structures and values of the
objects. A discriminant power function is combined with domain knowledge on
the features set at each iteration. It is shown that CSN consistently outperforms
methods such as identification trees and simplifies interactive classification
processes comparatively to search for K-Nearest-Neighbors method
Generation of knowledge about the control of a flow shop using data-analysis oriented learning techniques and simulation
Recherche de concepts à partir de données arborescentes et imprécises
n this article, we propose a formalism (ASN) to deal with imprecise and structured data described with attributes and imprecise values. The ASN allow us to represente entities that are composed with parts and sub-parts ; values may be imprecise, unknown and the attributes may be not applicable. We can also take into account constraints that exist between the values of the attributes. We aim to find concepts from a set of entities described with ASN. Concepts are defined from an extension of the Galois lattice theory to deal with imprecise and structured data. To find concepts, we propose an incremental algorithm that compute a lattice concepts extracted from the Galois lattice where the too general concepts? in regard to a given criteria? are not computed.Dans cet article, nous proposons un formalisme de reprĂ©sentation de donnĂ©es structurĂ©es et imprĂ©cises, les Arborescences Symboliques NuancĂ©es (ASN), qui est fondĂ© sur la notion d'attribut-valeur. Les ASN nous permettent de reprĂ©senter des entitĂ©s composĂ©es de parties et sous-parties dont les caractĂ©ristiques peuvent ĂȘtre imprĂ©cises, inconnues ou bien inapplicables et prenant en compte les liens pouvant exister entre les valeurs des diffĂ©rentes caractĂ©ristiques. Nous nous intĂ©ressons Ă la recherche de concepts Ă partir d'un ensemble d'entitĂ©s dĂ©crites par les ASN. La dĂ©finition des concepts repose sur une extension des treillis de Galois au cas de donnĂ©es arborescentes et nuancĂ©es. Pour rechercher les concepts, nous prĂ©sentons un algorithme incrĂ©mental permettant de calculer un treillis extrait du treillis de Galois en Ă©lagant les concepts trop gĂ©nĂ©raux