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Related Experiment Video

Updated: Jun 17, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

OP-ELM: optimally pruned extreme learning machine.

Yoan Miche¹, Antti Sorjamaa, Patrick Bas

¹Department of Information and Computer Science, Helsinki University of Technology, Espoo 02015, Finland. yoan.miche@tkk.fi

IEEE Transactions on Neural Networks

|December 17, 2009

Summary

This summary is machine-generated.

The optimally pruned extreme learning machine (OP-ELM) offers a faster, robust alternative for regression and classification tasks. This enhanced algorithm maintains accuracy comparable to support vector machines while significantly reducing computational time.

Related Experiment Videos

Last Updated: Jun 17, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Machine Learning
Artificial Intelligence
Computational Science

Background:

Extreme Learning Machine (ELM) is a popular algorithm for supervised learning.
Existing ELM methods may lack robustness and generalizability for diverse applications.
Need for improved algorithms that balance speed and accuracy in machine learning.

Purpose of the Study:

Introduce the Optimally Pruned Extreme Learning Machine (OP-ELM) methodology.
Enhance the original ELM algorithm for improved robustness and generality.
Evaluate OP-ELM performance against established machine learning algorithms.

Main Methods:

Developed the Optimally Pruned Extreme Learning Machine (OP-ELM) methodology.
Applied OP-ELM to various regression and classification problems.
Compared OP-ELM with original ELM, Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Gaussian Process (GP).

Main Results:

OP-ELM demonstrates significantly faster computational times compared to MLP, SVM, and GP.
Achieved accuracy comparable to Support Vector Machines (SVM).
OP-ELM maintains high performance in both regression and classification tasks.

Conclusions:

OP-ELM provides a robust and generic machine learning methodology.
Offers a substantial speed advantage over traditional algorithms, except the original ELM.
A publicly available OP-ELM toolbox facilitates its adoption and further research.