Result: Active Learning of Pareto Fronts
Title:
Active Learning of Pareto Fronts
Authors:
Source:
IEEE transactions on neural networks and learning systems (Print). 25(3):506-519
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2014.
Publication Year:
2014
Physical Description:
print, 29 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Electronics, Electronique, Computer science, Informatique, Psychology, psychopathology, psychiatry, Psychologie, psychopathologie, psychiatrie, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Théorie de la décision. Théorie de l'utilité, Decision theory. Utility theory, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Automatique théorique. Systèmes, Control theory. Systems, Modélisation et identification, Modelling and identification, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse régression, Regression analysis, Análisis regresión, Apprentissage supervisé, Supervised learning, Aprendizaje supervisado, Apprentissage(intelligence artificielle), Learning (artificial intelligence), Echantillonnage, Sampling, Muestreo, Ensemble dominant, Dominating set, Conjunto dominando, Estimation état, State estimation, Estimación estado, Identification système, System identification, Identificación sistema, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Modélisation, Modeling, Modelización, Optimum Pareto, Pareto optimum, Optimo Pareto, Processus Gauss, Gaussian process, Proceso Gauss, Programmation mathématique, Mathematical programming, Programación matemática, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Résolution problème, Problem solving, Resolución problema, Résultat expérimental, Experimental result, Resultado experimental, Scalarisation, Scalarization, Scalarisación, Système actif, Active system, Sistema activo, Système incertain, Uncertain system, Sistema incierto, Algorithmes à estimation de distribution, Estimation of distribution algorithm, Algoritmos de estimación de distribución, Active learning, Gaussian process regression, multiobjective optimization, uncertainty sampling
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Information Engineering and Computer Science, University of Trento, Trento 38123, Italy
ISSN:
2162-237X
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Operational research. Management
Operational research. Management
Accession Number:
edscal.28403984
Database:
PASCAL Archive
Further Information
This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multiobjective optimization problem. ALP casts the identification of the Pareto front into a supervised machine learning task. This approach enables an analytical model of the Pareto front to be built. The computational effort in generating the supervised information is reduced by an active learning strategy. In particular, the model is learned from a set of informative training objective vectors. The training objective vectors are approximated Pareto-optimal vectors obtained by solving different scalarized problem instances. The experimental results show that ALP achieves an accurate Pareto front approximation with a lower computational effort than state-of-the-art estimation of distribution algorithms and widely known genetic techniques.