Medical diagnosis research has recently focused on feature selection techniques due to the availability of multiple variables in medical datasets. Wrapper-based feature selection approaches have shown promise in provi...
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Medical diagnosis research has recently focused on feature selection techniques due to the availability of multiple variables in medical datasets. Wrapper-based feature selection approaches have shown promise in providing faster and more cost-effective predictors. However, selecting the most relevant features from medical datasets to increase disease classification accuracy remains a challenging research issue. To address this challenge, we propose an effective wrapper-based feature selection approach called BTLBO-KNN. It combines an improved binary Teaching-Learning Based optimization (BTLBO) algorithm with the K-Nearest Neighbor (KNN) classifier to accelerate the convergence rate in finding the near-optimal features subset. BTLBO-KNN incorporates two new efficient binary teaching and learning processes, an abandoned learner's replacement mechanism, and a teacher knowledge improvement method. We extensively compare BTLBO-KNN with recent state-of-the-art wrapper-based feature selection approaches on COVID-19 and 23 gene-expression and medical datasets with different dimensional complexities. Our results demonstrate the superiority of BTLBO-KNN over its alternatives in terms of minimizing the number of selected features and the classification error rate.
A new efficient binaryoptimization method based on Teaching-Learning-Based optimization (TLBO) algorithm is proposed to design an array of plasmonic nano bi-pyramids in order to achieve maximum absorption coefficient...
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A new efficient binaryoptimization method based on Teaching-Learning-Based optimization (TLBO) algorithm is proposed to design an array of plasmonic nano bi-pyramids in order to achieve maximum absorption coefficient spectrum. In binary TLBO, a group of learners consisting of a matrix with binary entries controls the presence (`1') or the absence (`0') of nanoparticles in the array. Simulation results show that absorption coefficient strongly depends on the localized position of plasmonic nanoparticles. Non-periodic structures have more appropriate response in term of absorption coefficient. This approach is useful in optical applications such as solar cells and plasmonic nano antenna.
Swarm-intelligence (SI) algorithms have received great attention in addressing various binaryoptimization problems such as feature selection. In this article, a new time-varying modified Sigmoid transfer function wit...
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Swarm-intelligence (SI) algorithms have received great attention in addressing various binaryoptimization problems such as feature selection. In this article, a new time-varying modified Sigmoid transfer function with two time-varying updating schemes is proposed as the binarization method for particle swarm optimization (PSO), grey wolf optimizationalgorithm (GWO), whale optimizationalgorithm (WOA), harris hawk optimization (HHO), and manta-ray foraging optimization (MRFO). The new binaryalgorithms, BPSO, BGWOA, BWOA, BHHO, and BMRFO algorithms are utilized for solving the descriptors selection problem in supervised Amphetamine-type Stimulants (ATS) drug classification task. The goal of this study is to improve the speed of convergence and classification accuracy. To evaluate the performance of the proposed methods, experiments were carried out on a specific chemical dataset containing molecular descriptors of ATS and non-ATS drugs. The results obtained showed that the proposed methods' performances on the chemical dataset are promising in near to optimal convergence, fast computation, increased classification accuracy, and enormous reduction in descriptor size.
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