%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 }