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The Impact of Randomization on Circular-Complex Extreme Learning Machine for Real Valued Classification Problems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 103 - Number 2 |
| Year of Publication: 2014 |
| Authors: Ram Govind Singh, Akhil Pandey |
10.5120/18043-8922
|
Ram Govind Singh, Akhil Pandey . The Impact of Randomization on Circular-Complex Extreme Learning Machine for Real Valued Classification Problems. International Journal of Computer Applications. 103, 2 ( October 2014), 1-7. DOI=10.5120/18043-8922
Abstract
Extreme Learning Machine (ELM) has recently emerged as a fast classifier giving good performance. Circular–Complex extreme learning machine (CC-ELM) is recently proposed complex variant of ELM which has fully complex activation function. It has been shown that CC-ELM outperforms real valued and other complex valued classifiers. In both CCELM & ELM parameters between input and hidden layer are initialized randomly and the weights between hidden and output layer are obtained analytically. Due to this randomization, the performance of both ELM & CC-ELM fluctuates. In this paper, performance fluctuation due to random parameter of CC-ELM and the circular transformation function have been analyzed first, then by using an Ensemble approach namely Bagging, a variants Bagging. C1 is proposed to bring the stability in the performance of CC-ELM. In Bagging. C1 various data samples are generated by using random parameters of circular transformation function. Performance of proposed classifier ensemble is evaluated using a set of benchmark real-valued classification problems from the University of California, Irvine machine learning repository.
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