Moga-SVM Implementation Comprising Three Core Modules

The Moga-SVM program comprises three core documents: 1. moga module handling multi-objective optimization 2. nsga module implementing non-dominated sorting genetic algorithms. Additionally, the implementation includes: - Data preprocessing routines for formatting input data for training and testing phases, including normalization and missing value handling - Feature selection algorithms applying filter/wrapper methods to identify optimal feature subsets from raw data - Model selection and parameter tuning processes utilizing genetic algorithms to optimize SVM kernel parameters (C, gamma) through cross-validation - Performance evaluation metrics implementing accuracy, precision, recall, F1-score, and ROC analysis to assess model performance across datasets - Result visualization methods generating confusion matrices, ROC curves, and decision boundary plots for intuitive model output representation Through these modular components and systematic procedures, the Moga-SVM implementation enables comprehensive SVM training, testing, and optimization, achieving enhanced predictive accuracy and classification performance in machine learning applications. The genetic algorithm integration specifically automates hyperparameter optimization while maintaining multiple objective balances.