Abstract: Extreme Learning Machine is a fast single layer feed forward neural network for real valued classification. It suffers from the problem of instability and over fitting. Extreme Learning Machine (ELM) has recently emerged as a fast classifier giving good performance. Voting based Extreme Learning Machine; VELM reduces this performance variation in Extreme Learning Machine by employing majority voting based ensembling technique.  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. This work further proposes and evaluates Voting based Extreme Learning Machine with Accuracy based ensemble Pruning, VELM_AP. VELM_AP generates component classifier in the same way as VELM. 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.

Keywords: Classification, Complex-Valued Neural Networks, Extreme Learning Machine, Ensemble Pruning

| DOI: 10.17148/IARJSET.2019.6510