Support Vector Machine Multiclass Classification Experimental System with Graphical User Interface

This is a Support Vector Machine multiclass classification experimental system with a graphical user interface. The system is entirely implemented in MATLAB, utilizing built-in functions like fitcecoc for multiclass SVM classification and incorporating custom code for data preprocessing and result visualization. It supports various classification tasks through configurable parameters including kernel selection (linear, RBF, polynomial) and regularization settings. As supplementary software for my graduation project, the system provides a user-friendly interface and feature-rich experimental environment for classification tasks. Users can conduct experiments and comparisons across different classification scenarios, gaining deeper insights into SVM algorithms and their multiclass classification applications. The system includes visualization tools and result analysis capabilities, enabling graphical representation of decision boundaries and performance metrics through MATLAB's plotting functions. Additionally, the system supports data import/export functions using MATLAB's file I/O capabilities, along with parameter configuration and saving features. This allows flexible experimental setup and result preservation according to specific requirements. The implementation includes error handling for invalid inputs and data validation routines to ensure robust operation. Overall, this system provides a comprehensive platform for SVM multiclass classification experiments, featuring modular code structure with separate functions for data handling, model training, and visualization components. It helps users better understand and apply SVM algorithms through practical experimentation and comparative analysis.