Experimental System for Support Vector Machine Pattern Recognition with Graphical User Interface

In this experimental system, we have developed a complete Support Vector Machine pattern recognition framework featuring an intuitive graphical user interface for various classification tasks. The system is entirely implemented in MATLAB, serving as one of the auxiliary programs for my graduation project. Beyond SVM, we integrated additional machine learning algorithms such as decision trees and random forests, employing cross-validation techniques and performance metrics (accuracy, precision, recall) for comparative analysis. The implementation includes core MATLAB functions like fitcsvm for SVM training and fitctree for decision trees, with parameter optimization routines.

The system incorporates practical functionalities including data visualization and feature selection modules. The visualization component utilizes MATLAB's plotting capabilities (scatter plots, confusion matrices) to help users understand data distribution and feature relationships, facilitating better feature engineering and model selection. The feature selection module implements algorithms like recursive feature elimination (RFE) and mutual information scoring to identify the most representative features from high-dimensional datasets, thereby enhancing model accuracy and generalization through dimensionality reduction techniques.

In summary, this experimental system represents a powerful, user-friendly, open-source machine learning application. It includes comprehensive documentation, complete source code with detailed comments, and demonstration datasets, making it suitable for both academic research and industrial applications. We believe this system, with its object-oriented design and modular architecture, will benefit machine learning enthusiasts and practitioners by promoting the development and application of machine learning technologies.