Title: An improved extreme learning machine to classify multinomial datasets using particle swarm optimisation
Authors: Nilamadhab Dash; Rojalina Priyadarshini; Rachita Misra
Addresses: Department of IT, C.V. Raman College of Engg., BidyaNagar, Mahura, Janla, Bhubaneswar-752054, Odisha, India ' Department of IT, C.V. Raman College of Engg., BidyaNagar, Mahura, Janla, Bhubaneswar-752054, Odisha, India ' Department of IT, C.V. Raman College of Engg., BidyaNagar, Mahura, Janla, Bhubaneswar-752054, Odisha, India
Abstract: In this paper, we propose a particle swarm-based extreme learning machine (ELM) to classify datasets with varying number of classes. This work emphasises on a couple of important parameters, like maximisation of classification accuracy and minimisation of training time. As a machine classifier, an ELM has been chosen, which is an improvement over back propagation network. For each of the input dataset an optimised target was determined by using particle swarm optimisation (PSO) technique. Those specific targets are used with the input data to train the ELM during classification process. For this, some of the benchmark classification datasets are used. To compare the proposed method and some of the existing methods an extensive experimental study has been carried out; a comparative analysis is done by taking parameters like percentage of classification accuracy, training time and complexity of the computing algorithm.
Keywords: multinomial classification; extreme learning machines; ELM; normalisation; particle swarm optimisation; PSO; back propagation neural networks; classification accuracy; input; targets; algorithm complexity.
International Journal of Intelligent Systems Design and Computing, 2017 Vol.1 No.1/2, pp.127 - 144
Received: 11 Mar 2015
Accepted: 18 Jan 2016
Published online: 10 Mar 2017 *