A Binary Chaotic Transient Search Optimization Algorithm for Enhancing Feature Selection

Real-world data mining problems require feature selection to improve efficiency and accuracy. Due to not considering characteristics of the feature selection problem itself, traditional mechanisms limit their performance on high-dimensional data. This paper proposes a novel feature selection algorithm based on binary transient search optimization (TSO) with chaotic maps. Owing to their pseudo-random behavior, chaotic systems can mimic randomness, which is crucial for addressing complex problems. The logistic equation is used to generate periodic sequences within the TSO evolutionary algorithm. Experimental evaluations are performed on 12 datasets from the UCI repository. On average, the modified TSO improves classification accuracy by 15% over traditional methods. Results demonstrate that the proposed method achieves higher accuracy, diversity, and convergence while reducing redundancy.