Feature selection is an effective way to decrease dataset dimensions and increase classification accuracy. However, feature selection is a complex and challenging procedure requiring a highly efficient algorithm.  In this enhanced bat algorithm (ICBA), the issue of feature selection is initially conceptualized and subsequently transformed into a fitness function, which assesses the quality of feature subsets based on how well they improve classification performance. We also propose an ICBA to address the issue of feature selection. To enhance the BA and expand its applicability to feature selection issues, we integrated a chaotic map into the BA. Ultimately The proposed algorithm ICBA is benchmarked against binary PSO (BPSO), Binary dragonfly algorithm (BDA), Binary grey wolf optimization approach (BGWO), Binary bat algorithm (BBA), and enhanced binary bat algorithm (EBBA). To evaluate these algorithms, five datasets were sourced from the UC Irvine Machine Learning Repository. The experimental findings reveal that the ICBA algorithm outperforms other comparative algorithms across all datasets. In the Breastcancer dataset, the accuracy rate for ICBA was (0.9941) compared to the closest algorithm's (0.9786). In the BreastEW dataset, the accuracy rate for ICBA was (0.9857) compared to the closest algorithm's (0.9614). In the Congress dataset, the accuracy rate for ICBA was (0.9893), whereas it was (0.9793) for the nearest algorithm. In the SpectEW dataset, the accuracy rate for ICBA was (0.8691) compared to the nearest algorithm, where it was (0.7407). In the tic-tac-toe dataset, the accuracy rate was (0.9688), while the closest algorithm was (0.8521).