Wrapper-Based Feature Selection for Medical Diagnosis: The BTLBO-KNN Algorithm

نویسندگان

چکیده

Medical diagnosis research has recently focused on feature selection techniques due to the availability of multiple variables in medical datasets. Wrapper-based approaches have shown promise providing faster and more cost-effective predictors. However, selecting most relevant features from datasets increase disease classification accuracy remains a challenging issue. To address this challenge, we propose an effective wrapper-based approach called BTLBO-KNN. It combines improved Binary Teaching-Learning Based Optimization (BTLBO) algorithm with K-Nearest Neighbor (KNN) classifier accelerate convergence rate finding near-optimal subset. BTLBO-KNN incorporates two new efficient binary teaching learning processes, abandoned learner’s replacement mechanism, teacher knowledge improvement method. We extensively compare recent state-of-the-art COVID-19 23 gene-expression different dimensional complexities. Our results demonstrate superiority over its alternatives terms minimizing number selected error rate.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2023

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2023.3287484