• Elastic net orthogonal forward...

Result: Elastic net orthogonal forward regression

Title:
Elastic net orthogonal forward regression
Authors:
XIA HONG, SHENG CHEN
Source:
Neurocomputing (Amsterdam). 148:551-560
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 48 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Inférence linéaire, régression, Linear inference, regression, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Ajustement modèle, Model matching, Ajustamiento modelo, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse régression, Regression analysis, Análisis regresión, Choix procédé, Process selection, Selección proceso, Classification, Clasificación, Erreur quadratique moyenne, Mean square error, Error medio cuadrático, Estimation paramètre, Parameter estimation, Estimación parámetro, Fonction coût, Cost function, Función coste, Identification système, System identification, Identificación sistema, Indice aptitude, Capability index, Indice aptitud, Intelligence en essaim, Swarm intelligence, Inteligencia de enjambre, Modèle linéaire, Linear model, Modelo lineal, Modélisation, Modeling, Modelización, Méthode itérative, Iterative method, Método iterativo, Méthode pas à pas, Step by step method, Método paso a paso, Optimisation PSO, Particle swarm optimization, Optimización PSO, Polynôme orthogonal, Orthogonal polynomial, Polinomio ortogonal, Sélection automatique, Automatic selection, Selección automática, Sélection modèle, Model selection, Selección modelo, Tolérance faute, Fault tolerance, Tolerancia falta, Validation croisée, Cross validation, Validación cruzada, Elastic net, Forward regression, Leave one out errors, Linear-in-the-parameters model, Regularisation
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
School of Systems Engineering, University of Reading, United Kingdom
School of Electronics and Computer Science, University of Southampton, SO17 1BJ, United Kingdom
Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
ISSN:
0925-2312
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28844568
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
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.28844568
Database:
PASCAL Archive

Further Information

An efficient two-level model identification method aiming at maximising a model's generalisation capability is proposed for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularisation parameters in the elastic net are optimised using a particle swarm optimisation (PSO) algorithm at the upper level by minimising the leave one out (LOO) mean square error (LOOMSE). There are two elements of original contributions. Firstly an elastic net cost function is defined and applied based on orthogonal decomposition, which facilitates the automatic model structure selection process with no need of using a predetermined error tolerance to terminate the forward selection process. Secondly it is shown that the LOOMSE based on the resultant ENOFR models can be analytically computed without actually splitting the data set, and the associate computation cost is small due to the ENOFR procedure. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.