Classification Methods Toolbox with GUI Implementation

The classification_matlab_toolbox provides a highly intuitive MATLAB-based platform for implementing various classification algorithms through a user-friendly GUI interface. This toolbox is specifically designed to streamline data classification workflows, which constitutes a fundamental task in machine learning applications. The implementation includes core classification algorithms such as decision trees (using MATLAB's fitctree function), support vector machines (via fitcsvm with customizable kernel functions), and additional ensemble methods. The GUI components are built using MATLAB's App Designer framework, allowing users to visually select parameters, preprocess data, and visualize classification results without direct coding. The toolbox incorporates cross-validation routines (through crossval function) and performance metrics calculation (like confusionmat for accuracy assessment). Comprehensive documentation includes code examples demonstrating feature extraction techniques, hyperparameter tuning methods, and real-time results visualization. Additionally, the package contains tutorial scripts illustrating complete classification pipelines from data loading to model evaluation, making it particularly suitable for both educational and research purposes in pattern recognition and predictive modeling.