MATLAB Toolbox for Optimal Pruned Extreme Learning Machine

The Optimal Pruned Extreme Learning Machine (OP-ELM) is a sophisticated machine learning algorithm, and this MATLAB toolbox provides researchers with direct access to its source code for customization and application. The toolbox includes core implementation functions for neural network pruning, weight optimization, and model selection algorithms, allowing users to thoroughly understand OP-ELM's working mechanism through practical code examination. Through this toolbox, users can efficiently apply OP-ELM to various datasets while leveraging built-in utilities for data preprocessing, feature selection, and performance evaluation. The modular code structure enables easy modification of key parameters including hidden layer neurons, pruning criteria, and regularization methods. Additionally, the toolbox contains specialized functions for cross-validation, model comparison, and result visualization to streamline experimental workflows. For machine learning practitioners interested in efficient neural network architectures, this toolbox serves as an invaluable resource for both educational and research purposes, providing comprehensive implementation examples and optimization techniques.