IntelELM: A python framework for intelligent metaheuristic-based extreme learning machine - Van Thieu, Nguyen; Houssein, Essam H.; Oliva, Diego; Hung, Nguyen Duy (Neurocomputing (Amsterdam), 2025)
This study introduces IntelELM, an open-source Python library designed for hybrid neural networks that integrate Extreme Learning Machine (ELM) with Metaheuristic Algorithms (MHAs). Built on the foundations of two well-established libraries, Scikit-Learn and Mealpy, IntelELM offers four primary strategies for addressing regression and classification tasks. These strategies are implemented through the ElmRegressor and ElmClassifier classes for traditional ELM, as well as the MhaElmRegressor and MhaElmClassifier for hybrid metaheuristic-based ELM models. The library is easy to install and use, especially for individuals familiar with the Scikit-Learn ecosystem. IntelELM comprises at least 402 distinct models across these four primary classes, encompassing classical ELM regression and classification models, as well as over 200 metaheuristic-based ELM regression and classification models each. To demontrade the power of the proposed library, we evaluate several hybrid models from the IntelELM library alongside traditional machine learning models across three benchmark datasets. Experimental results demonstrate that the hybrid models within IntelELM exhibit competitive performance across various performance metrics compared to traditional machine learning approaches. These findings underscore the library's potential to offer effective solutions to real-world problems and contribute significantly to the computer science community. We have released the source code of the library as open-source, inviting the research community to conduct widespread evaluations of this comprehensive framework as a promising tool for research studies and real-world solutions. The source code can be found at https://github.com/thieu1995/IntelELM. •We propose hybrid-based Extreme Learning Machine (ELM) software called IntelELM.•IntelELM provides classical and metaheuristic-based ELM models for diverse tasks.•Easy installation and use for those familiar with the Scikit-Learn ecosystem.•Hybrid models in IntelELM show competitive performance on benchmark datasets.•IntelELM's potential for effective real-world problems and ML community impact.