Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimization
Open Access
- 1 January 2022
- journal article
- research article
- Published by Walter de Gruyter GmbH in Nonlinear Engineering
- Vol. 11 (1), 568-581
- https://doi.org/10.1515/nleng-2022-0257
Abstract
In order to classify data and improve extreme learning machine (ELM), this study explains how a hybrid optimization-driven ELM technique was devised. Input data are pre-processed in order to compute missing values and convert data to numerical values using the exponential kernel transform. The Jaro–Winkler distance is used to identify the relevant features. The feed-forward neural network classifier is used to categorize the data, and it is trained using a hybrid optimization technique called the modified enhanced Invasive Weed, a meta heuristic algorithm, and Cuckoo Search, a non-linear optimization algorithm ELM. The enhanced Invasive Weed optimization (IWO) algorithm and the enhanced Cuckoo Search (CS) algorithm are combined to create the modified CSIWO. The experimental findings presented in this work demonstrate the viability and efficacy of the created ELM method based on CSIWO, with good experimental result as compared to other ELM techniques.Keywords
This publication has 36 references indexed in Scilit:
- Extreme Learning Machine for Multilayer PerceptronIEEE Transactions on Neural Networks and Learning Systems, 2015
- Trends in extreme learning machines: A reviewNeural Networks, 2015
- An Insight into Extreme Learning Machines: Random Neurons, Random Features and KernelsCognitive Computation, 2014
- Weighted extreme learning machine for imbalance learningNeurocomputing, 2012
- Hellinger distance decision trees are robust and skew-insensitiveData Mining and Knowledge Discovery, 2011
- Engineering optimisation by cuckoo searchInternational Journal of Mathematical Modelling and Numerical Optimisation, 2010
- Exploratory Undersampling for Class-Imbalance LearningIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008
- Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden NodesIEEE Transactions on Neural Networks, 2006
- Extreme learning machine: Theory and applicationsNeurocomputing, 2006
- Generalization of backpropagation with application to a recurrent gas market modelNeural Networks, 1988