Ender SEVİNÇ Tansel DÖKEROĞLU2024-07-112024-07-1120191300-0632https://acikarsiv.thk.edu.tr/handle/123456789/2086Data classification is the process of organizing data by relevant categories. In this way, the data can beunderstood and used more efficiently by scientists. Numerous studies have been proposed in the literature for the problem of data classification. However, with recently introduced metaheuristics, it has continued to be riveting to revisit this classical problem and investigate the efficiency of new techniques. Teaching-learning-based optimization (TLBO) is a recent metaheuristic that has been reported to be very effective for combinatorial optimization problems. In this study, we propose a novel hybrid TLBO algorithm with extreme learning machines (ELM) for the solution of data classification problems. The proposed algorithm (TLBO-ELM) is tested on a set of UCI benchmark datasets. The performance of TLBO-ELM is observed to be competitive for both binary and multiclass data classification problems compared with state-of-the-art algorithms.eng[Fen > Mühendislik > Mühendislik, Elektrik ve Elektronik, Fen > Mühendislik > Bilgisayar Bilimleri, Yazılım Mühendisliği, Fen > Mühendislik > Bilgisayar Bilimleri, Sibernitik, Fen > Mühendislik > Bilgisayar Bilimleri, Bilgi Sistemleri, Fen > Mühendislik > Bilgisayar Bilimleri, Donanım ve Mimari, Fen > Mühendislik > Bilgisayar Bilimleri, Teori ve Metotlar, Fen > Mühendislik > Bilgisayar Bilimleri, Yapay Zeka]Makale1300-0632