A hybrid evolutionary functional link artificial neural network for data mining and classification

By Faissal Mili, Manel Hamdi. 2012

24 citations recorded by the source corpus.

This paper presents a specific structure of neural network as the functional link artificial neural network (FLANN). This technique has been employed for classification task 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.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Pradipta Kishore Dash
2. Ranjeeta Bisoi
3. Ajit Kumar Rout
4. Rajashree Dash

Citations in the corpus

• Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach (2015, methodology): “A wide variety of FLANNs with functional expansion using orthogonal trigonometric functions (Dehuri et al., 2012; Mili and Hamdi, 2012; Patra et al., 2009), using Chebyshev polynomial (Mishra et al., 2009; Jiang et al., 2012; Li et al., 2012), using Laguerre polynomial (Chandra et al., 2009) and…”