A hybrid Evolutionary Functional Link Artificial Neural Network for Data mining and Classification
M. Faissal MILI. International Journal of Advanced Computer Science and Applications(IJACSA), (2012)
Abstract
This paper presents a specific structure of neural network as the functional link artificial neural network (FLANN). This technique has been employed for classification tasks of data mining. In fact, there are a few studies that used this tool for solving classification problems. In this present research, we propose a hybrid FLANN (HFLANN) model, where the optimization process is performed using 3 known population based techniques such as genetic algorithms, particle swarm and differential evolution. This model will be empirically compared to FLANN based back-propagation algorithm and to others classifiers as decision tree, multilayer perceptron based back-propagation algorithm, radical basic function, support vector machine, and K-nearest Neighbor. Our results proved that the proposed model outperforms the other single model. (Abstract)
%0 Journal Article
%1 IJACSA.2012.030815
%A Faissal MILI, Manel HAMDI
%D 2012
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K Classification; Data Differential Functional Particle algorithms; artificial component evolution genetic link mining; network; neural swarm;
%N 8
%T A hybrid Evolutionary Functional Link Artificial Neural Network for Data mining and Classification
%U http://ijacsa.thesai.org/
%V 3
%X This paper presents a specific structure of neural network as the functional link artificial neural network (FLANN). This technique has been employed for classification tasks of data mining. In fact, there are a few studies that used this tool for solving classification problems. In this present research, we propose a hybrid FLANN (HFLANN) model, where the optimization process is performed using 3 known population based techniques such as genetic algorithms, particle swarm and differential evolution. This model will be empirically compared to FLANN based back-propagation algorithm and to others classifiers as decision tree, multilayer perceptron based back-propagation algorithm, radical basic function, support vector machine, and K-nearest Neighbor. Our results proved that the proposed model outperforms the other single model. (Abstract)
@article{IJACSA.2012.030815,
abstract = {This paper presents a specific structure of neural network as the functional link artificial neural network (FLANN). This technique has been employed for classification tasks of data mining. In fact, there are a few studies that used this tool for solving classification problems. In this present research, we propose a hybrid FLANN (HFLANN) model, where the optimization process is performed using 3 known population based techniques such as genetic algorithms, particle swarm and differential evolution. This model will be empirically compared to FLANN based back-propagation algorithm and to others classifiers as decision tree, multilayer perceptron based back-propagation algorithm, radical basic function, support vector machine, and K-nearest Neighbor. Our results proved that the proposed model outperforms the other single model. (Abstract)},
added-at = {2014-02-21T08:00:08.000+0100},
author = {{Faissal MILI}, Manel HAMDI},
biburl = {https://www.bibsonomy.org/bibtex/2116bbc52b9ca561ec14cc4fe18ad9121/thesaiorg},
interhash = {1b814047b26ab84dfceab6f4bac26dcc},
intrahash = {116bbc52b9ca561ec14cc4fe18ad9121},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {Classification; Data Differential Functional Particle algorithms; artificial component evolution genetic link mining; network; neural swarm;},
number = 8,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{A hybrid Evolutionary Functional Link Artificial Neural Network for Data mining and Classification}},
url = {http://ijacsa.thesai.org/},
volume = 3,
year = 2012
}