An Adapted Ant Colony Optimization for Feature Selection

Abstract

As information technologies evolve, they generate vast and ever-expanding datasets. This wealth of high-dimensional data presents challenges, including increased computational demands and difficulties in extracting valuable insights. The aim of feature selection is to address this complexity by reducing data dimensions with minimal information loss. Our proposed feature selection approach, the Feature Selection via Ant Colony Optimization algorithm, employs heuristic distance directly in its probability function, instead of using its inverse. The algorithm bypasses the need for sub-attribute sets, running multiple iterations to create a frequency order list from the collected routes, which informs feature importance. The efficacy of this technique has been validated through comparative experiments with other methods from scientific literature. To ensure fairness, these experiments used identical datasets, data partitioning strategies, classifiers, and performance metrics. Initially, the algorithm was compared with fifteen different algorithms, and subsequently benchmarked against three selected methods. The impact of feature selection on classification performance was statistically verified through comparisons before and after the feature selection process. Convergence performance of the proposed method has also been evaluated. Our findings robustly support the efficacy of the introduced approach in managing complex, multidimensional data effectively.