Sequential Backward Elimination Feature Selection Algorithm

This article explores five distinct feature selection algorithms: Sequential Backward Elimination, Sequential Floating Forward Selection (SFFS), Information Significance Detection (ISD), Log-Likelihood Ratio (LLR), and Chi-Square Zhang Detection (CZD). The Sequential Backward Elimination algorithm employs a backward search strategy, iteratively removing the least significant features to improve model performance through wrapper-based evaluation. SFFS combines forward and backward search phases, dynamically adding and removing features while maintaining optimal feature subsets using conditional inclusion/exclusion criteria. ISD operates on information-theoretic principles, calculating entropy-based metrics to identify features with maximum relevance to the target variable. The LLR algorithm utilizes log-likelihood ratios to quantify feature-target correlations, particularly effective for categorical data analysis. Finally, CZD implements chi-square statistical testing to assess feature significance, determining independence between features and target variables through contingency table analysis. These algorithms enable comprehensive dataset analysis and optimal feature subset selection for building more accurate predictive models.