➡️ Implementation of of proposed LSTELM and WLTELM models. These are ELM models developed for binary classification problems.
➡️ The LSTELMfunc and WLTELMfunc functions implement the LSTELM and WLTELM models in MATLAB. For a demo, collect all files in a single folder. Add that folder to path and run "demo_script.m"
➡️ For more details about the LSTLEM and WLTELM models, search the research article 'Time efficient variants of Twin Extreme Learning Machine'
➡️ If you use LSTELM and WLTELM's Model, it's concepts and functionalities in a scientific publication, please cite the following paper:
Pritam Anand, Amisha Bharti, Reshma Rastogi, "Time efficient variants of Twin Extreme Learning Machine", Intelligent Systems with Applications, Volume 17, 2023, ISSN 2667-3053, https://doi.org/10.1016/j.iswa.2022.200169.
BibTeX entry:
@article{ANAND2023200169,
title = {Time efficient variants of Twin Extreme Learning Machine},
journal = {Intelligent Systems with Applications},
volume = {17},
pages = {200169},
year = {2023},
issn = {2667-3053},
doi = {https://doi.org/10.1016/j.iswa.2022.200169},
url = {https://www.sciencedirect.com/science/article/pii/S2667305322001065},
author = {Pritam Anand and Amisha Bharti and Reshma Rastogi},
keywords = {Classification, Extreme Learning Machine, Twin Support Vector Machine, Twin Extreme Learning Machine},
abstract = {Twin Extreme Learning Machine models can obtain better generalization ability than the standard Extreme Learning Machine model. But, they require to solve a pair of quadratic programming problems for this. It makes them more complex and computationally expensive than the standard Extreme Learning Machine model. In this paper, we propose two novel time-efficient formulations of the Twin Extreme Learning Machine, which only require the solution of systems of linear equations for obtaining the final classifier. In this sense, they can combine the benefits of the Twin Support Vector Machine and standard Extreme Learning Machine in the true sense. We term our first formulation as ‘Least Squared Twin Extreme Learning Machine’. It minimizes the L2-norm of error variables in its optimization problem. Our second formulation ‘Weighted Linear loss Twin Extreme Learning Machine’ uses the weighted linear loss function for calculating the empirical error, which makes it insensitive towards outliers. Numerical results obtained with multiple benchmark datasets show that proposed formulations are time efficient with better generalization ability. Further, we have used the proposed formulations in the detection of phishing websites and shown that they are much more effective in the detection of phishing websites than other Extreme Learning Machine models.}
}