An Efficient Optimization Method for Extreme Learning Machine Using Artificial Bee Colony
Chao Ma
Journal of Digital Information Management, Vol. 15, No. 3, 2017.
DOI: 
Link: http://dline.info/fpaper/jdim/v15i3/jdimv15i3_4.pdf
Abstract:
Recently extreme learning machine (ELM)
was proposed as a new learning method for single hiddenlayer
feedforward neural networks (SLFNs), it is not the
same as traditional gradient based learning algorithm
strategies as it can achieve good generalization
performance as well as extremely fast learning speed.
However, ELM may require large number of hidden neurons
due to the random determination of the input weights and
hidden biases, and there may exist a set of non-optimal
parameters which lead ELM not be able to reach the global
optimum in some cases. With the help of ideas that using
a hybrid approach which takes advantage of the
optimization method and ELM to train SLFNs, this study
proposes a novel hybrid approach based on artificial bee
colony (ABC) optimization method to optimize the ELM
parameters, where the optimal input weights and biases
of ELM are specified by the ABC approach and Moore-
Penrose (MP) generalized inverse to analytically determine
the output weights. The proposed algorithm, named ABCELM,
is rigorously compared with the original ELM and
other evolutionary ELM methods in different classification
datasets. The obtained results clearly confirm that the
proposed approach is more suitable for classification
problems that we investigated, and it can not only achieve
better generalization performance but be more robust with
much more compact networks.