Welcome to IntelELM’s documentation!

IntelELM: A Python Framework for Intelligent Metaheuristic-based Extreme Learning Machine

IntelELM (Intelligent Metaheuristic-based Extreme Learning Machine) is a Python library that implements a framework for training Extreme Learning Machine (ELM) networks using Metaheuristic Algorithms. It provides a comparable alternative to the traditional ELM network and is compatible with the Scikit-Learn library. With IntelELM, you can perform searches and hyperparameter tuning using the functionalities provided by the Scikit-Learn library.

• Free software: GNU General Public License (GPL) V3 license

• Provided Estimator: ElmRegressor, ElmClassifier, MhaElmRegressor, MhaElmClassifier

• Total Optimization-based ELM Regression: > 200 Models

• Total Optimization-based ELM Classification: > 200 Models

• Supported datasets: 54 (47 classifications and 7 regressions)

• Supported performance metrics: >= 67 (47 regressions and 20 classifications)

• Supported objective functions (as fitness functions or loss functions): >= 67 (47 regressions and 20 classifications)

• Documentation: https://intelelm.readthedocs.io/en/latest/

• Python versions: >= 3.7.x

• Dependencies: numpy, scipy, scikit-learn, pandas, mealpy, permetrics

Quick Start:

• Installation
• Tutorials
  • 1) Getting started in 30s
  • 2) Model Definition
  • 3) Data Preparation
  • 4) Scikit-Learn Integration

Models API:

• IntelELM Library
  • intelelm.base_elm module
  • Subpackages
    • intelelm.model package
      • intelelm.model.standard_elm module
      • intelelm.model.mha_elm module
    • intelelm.utils package
      • intelelm.utils.activation module
      • intelelm.utils.data_loader module
      • intelelm.utils.encoder module
      • intelelm.utils.evaluator module
      • intelelm.utils.validator module

Support:

• Citation Request
• Important links
• License

Indices and tables

• Index

• Module Index

• Search Page