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A hybrid evolutionary functional link artificial neural network for data mining and classification.
| Content Provider | CiteSeerX |
|---|---|
| Author | Mili, Faissal Hamdi, Manel |
| Abstract | 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 backpropagation algorithm, radical basic function, support vector machine, and K-nearest Neighbor. Our results proved that the proposed model outperforms the other single model. Keywords- component Data mining; Classification; Functional link artificial neural network; genetic algorithms; Particle swarm; Differential evolution. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Data Mining Particle Swarm Genetic Algorithm Differential Evolution Decision Tree Keywords Component Data Mining Multilayer Perceptron Functional Link Artificial Neural Network Neural Network Radical Basic Function Classification Problem Others Classifier Backpropagation Algorithm Optimization Process Classification Task Artificial Neural Network Single Model Hybrid Flann K-nearest Neighbor Support Vector Machine Back-propagation Algorithm Present Research Specific Structure |
| Content Type | Text |
| Resource Type | Article |
