Relief Algorithm: A Revised Feature Selection Approach

This article presents a revised feature selection algorithm known as the Relief algorithm. The Relief algorithm operates as an instance-weighting-based feature selection method that evaluates feature relevance by measuring the correlation between features and target categories. Unlike many traditional feature selection methods, Relief demonstrates superior performance in handling both classification tasks (through distance-based neighbor analysis) and regression problems (via appropriate distance metric adaptations). The algorithm implementation typically involves iterating through instances, calculating feature weight updates based on nearest-hit (same-class) and nearest-miss (different-class) comparisons using Euclidean or Manhattan distance metrics. We further examine the algorithm's advantages, including its simplicity, noise tolerance, and ability to detect feature interactions, while addressing limitations such as sensitivity to redundant features and computational complexity with large datasets. Performance enhancement strategies discussed include reliefF extensions for multi-class problems, kernel-based distance adaptations, and parallel computing implementations for scalability. Through this comprehensive analysis, readers gain deeper insights into Relief algorithm's practical applications and significance in modern feature selection pipelines.