Custom Feature Selection Algorithms Implementation

I have developed a comprehensive feature selection program that implements four distinct algorithmic approaches: Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Plus-l Minus-r (l-r) method, and Sequential Floating Forward Selection (SFFS). These algorithms systematically evaluate feature subsets to identify the most relevant features from datasets, enabling more accurate data analysis and predictive modeling. The implementation includes proper scoring mechanisms to assess feature relevance and iterative procedures to optimize subset selection. Feature selection serves as a critical preprocessing step in data mining and machine learning workflows by reducing dimensionality, minimizing noise and redundancy, while enhancing model performance and generalization capabilities. The program architecture incorporates cross-validation functionality to ensure robust feature evaluation and prevent overfitting. Additionally, visualization modules are integrated to help users intuitively understand feature selection principles and results through graphical representations of feature importance scores and subset performance metrics. The code implementation follows modular design principles with separate classes for each algorithm, featuring configurable parameters for flexibility and comprehensive logging for result tracking. Key functions include feature ranking evaluation, subset generation algorithms, and performance metric calculations that work with various dataset formats commonly used in machine learning applications.