Treffer: Immune multiobjective optimization algorithm for unsupervised feature selection
Title:
Immune multiobjective optimization algorithm for unsupervised feature selection
Authors:
Source:
Applications of evolutionary computing (EvoWorkshops 2006)Lecture notes in computer science. :484-494
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 15 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Bioinformatics, Bioinformatique, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, 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, Algorithme immunitaire, Immune algorithm, Algoritmo inmunitario, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse amas, Cluster analysis, Analisis cluster, Apprentissage non supervisé, Unsupervised learning, Base donnée, Database, Base dato, Classification, Clasificación, Clone, Clona, Fonction objectif, Objective function, Función objetivo, Optimisation, Optimization, Optimización, Problème recherche, Search problem, Problema investigación, Programmation mathématique, Mathematical programming, Programación matemática, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Système immunitaire, Immune system, Sistema inmunitario
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
National Key Lab for Radar Signal Processing, Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China
ISSN:
0302-9743
Rights:
Copyright 2007 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
Accession Number:
edscal.19131281
Database:
PASCAL Archive
Weitere Informationen
A feature selection method for unsupervised learning is proposed. Unsupervised feature selection is considered as a combination optimization problem to search for the suitable feature subset and the pertinent number of clusters by optimizing the efficient evaluation criterion for clustering and the number of features selected. Instead of combining these measures into one objective function, we make use of the multiobjective immune clonal algorithm with forgetting strategy to find the more discriminant features for clustering and the most pertinent number of clusters. The results of experiments on synthetic data and real datasets from UCI database show the effectiveness and potential of the method.