Two swarm intelligence approaches for tuning extreme learning machine
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Extreme learning machine (ELM) is a new algorithm for training single-hidden layer feedforward neural networks which provides good performance as well as fast learning speed. ELM tends to produce good generalization performance with large number of hidden neurons as the input weights and hidden neurons biases are randomly initialized and remain unchanged during the learning process, and the output weights are analytically determined. In this paper, two swarm intelligence based metaheuristic techniques, viz. Artificial Bee Colony (ABC) and Invasive Weed Optimization (IWO) are proposed for tuning the input weights and hidden biases. The proposed approaches are called ABC-ELM and IWO-ELM in which the input weights and hidden biases are selected using ABC and IWO respectively and the output weights are computed using the Moore-Penrose (MP) generalized inverse. The proposed approaches are tested on different benchmark classification data sets and simulations show that the proposed approaches obtain good generalization performance in comparison to the other techniques available in the literature.
KeywordsArtificial bee colony algorithm Classification Extreme learning machine Invasive weed optimization Swarm intelligence
- 1.Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: A new learning scheme of feedforward neural networks. In: Proceedings of International Joint Conference on Neural Networks (IJCNN), vol 2, Budapest, Hungary, pp 985–990Google Scholar
- 14.Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. technical report tr06, Computer Engineering Department. Erciyes University, TurkeyGoogle Scholar
- 17.Basak A, Pal S, Das S, Abraham A, Snasel V (2010) A modified invasive weed optimization algorithm for time-modulated linear antenna array synthesis. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC). Barcelona, pp 1–8Google Scholar
- 19.Schmidt WF, Kraaijveld MA, Duin RP (1992) Feedforward neural networks with random weights. In: Proceedings of 11th IAPR International Conference on Pattern Recognition Methodology and Systems. Hague, Netherlands, pp 1–4Google Scholar