  Mixture of experts method is a neural network based ensemble learning that
has great ability to improve the overall classification accuracy. This method
is based on the divide and conquer principle, in which the problem space is
divided between several experts by supervisition of gating network. In this
paper, we propose an ensemble learning method based on mixture of experts which
is named mixture of ELM based experts with trainable gating network (MEETG) to
improve the computing cost and to speed up the learning process of ME. The
structure of ME consists of multi layer perceptrons (MLPs) as base experts and
gating network, in which gradient-based learning algorithm is applied for
training the MLPs which is an iterative and time consuming process. In order to
overcome on these problems, we use the advantages of extreme learning machine
(ELM) for designing the structure of ME. ELM as a learning algorithm for single
hidden-layer feed forward neural networks provides much faster learning process
and better generalization ability in comparision with some other traditional
learning algorithms. Also, in the proposed method a trainable gating network is
applied to aggregate the outputs of the experts dynamically according to the
input sample. Our experimental results and statistical analysis on 11 benchmark
datasets confirm that MEETG has an acceptable performance in classification
problems. Furthermore, our experimental results show that the proposed approach
outperforms the original ELM on prediction stability and classification
accuracy.
