Chicken Swarm-Based Feature Subset Selection with Optimal Machine Learning Enabled Data Mining Approach

Data mining (DM) involves the process of identifying patterns, correlation, and anomalies existing in massive datasets. The applicability DM includes several areas such as education, healthcare, business, finance. Educational Mining (EDM) is an interdisciplinary domain which focuses on DM, machine learning (ML), statistical approaches for pattern recognition quantities educational data. This type data suffers from curse dimensionality problems. Thus, feature selection (FS) become essential. study designs a Feature Subset Selection with optimal model (FSSML-EDM). proposed method three major processes. At initial stage, presented FSSML-EDM uses Chicken Swarm Optimization-based (CSO-FS) technique electing subsets. Next, extreme (ELM) classifier employed classification Finally, Artificial Hummingbird (AHB) algorithm utilized adjusting parameters involved ELM model. performance revealed that achieves better results compared other models under dimensions.