Treffer: A new evolutionary neural network classifier

Title:
A new evolutionary neural network classifier
Authors:
THAMMANO, Arit, MEENGEN, Asavin
Source:
Advances in knowledge discovery and data mining (Hanoi, 18-20 May 2005)Lecture notes in computer science. :249-255
Publisher Information:
Berlin: Springer, 2005.
Publication Year:
2005
Physical Description:
print, 10 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, 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, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme flou, Fuzzy algorithm, Algoritmo borroso, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse donnée, Data analysis, Análisis datos, Classification, Clasificación, Découverte connaissance, Knowledge discovery, Descubrimiento conocimiento, Fouille donnée, Data mining, Busca dato, Modélisation, Modeling, Modelización, Réseau neuronal flou, Fuzzy neural nets, Réseau neuronal, Neural network, Red neuronal, Théorie résonance adaptative, Adaptive resonance theory
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Faculty of Information Technology King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16895517
Rights:
Copyright 2005 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.16895517
Database:
PASCAL Archive

Weitere Informationen

This paper proposes two new concepts: (1) the new evolutionary algorithm and (2) the new approach to deal with the classification problems by applying the concepts of the fuzzy c-means algorithm and the evolutionary algorithm to the artificial neural network. During training, the fuzzy c-means algorithm is initially used to form the clusters in the cluster layer; then the evolutionary algorithm is employed to optimize those clusters and their parameters. During testing, the class whose cluster node returns the maximum output value is the result of the prediction. This proposed model has been benchmarked against the standard backpropagation neural network, the fuzzy ARTMAP, C4.5, and CART. The results on six benchmark problems are very encouraging.