A novel chaotic binary butterfly optimization algorithm based feature selection model for classification of autism spectrum disorder - Ramakrishnan, Anandkumar; Ramalingam, Rajakumar; Ramalingam, Padmanaban; Ravi, Vinayakumar; Alahmadi, Tahani Jaser; Maidin, Siti Sarah (Zielona Góra: Uniwersytet Zielonogórski)
Autism spectrum disorder (ASD) issues formidable challenges in early diagnosis and intervention, requiring efficient methods for identification and treatment. By utilizing machine learning, the risk of ASD can be accurately and promptly evaluated, thereby optimizing the analysis and expediting treatment access. However, accessing high dimensional data degrades the classifier performance. In this regard, feature selection is considered an important process that enhances the classifier results.
Keywords: data classification; feature selection; metaheuristics; machine learning; autism spectrum disorder