Search alternatives:
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
also feature » a feature (Expand Search), all features (Expand Search), wise feature (Expand Search)
binary base » binary mask (Expand Search), ciliary base (Expand Search), binary image (Expand Search)
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
also feature » a feature (Expand Search), all features (Expand Search), wise feature (Expand Search)
binary base » binary mask (Expand Search), ciliary base (Expand Search), binary image (Expand Search)
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61
Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm
Published 2025“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”
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62
Pseudo Code of RBMO.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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63
P-value on CEC-2017(Dim = 30).
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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64
Memory storage behavior.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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65
Elite search behavior.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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66
Description of the datasets.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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67
S and V shaped transfer functions.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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68
S- and V-Type transfer function diagrams.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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69
Collaborative hunting behavior.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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70
Friedman average rank sum test results.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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71
IRBMO vs. variant comparison adaptation data.
Published 2025“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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72
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74
Table_1_bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.docx
Published 2023“…In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.…”
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75
Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf
Published 2024“…To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”
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76
Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish)
Published 2024“…</a></p><p dir="ltr">More information:</p><ul><li><a href="https://www.hatemedia.es/" rel="nofollow" target="_blank">https://www.hatemedia.es/</a> or contact: <a href="mailto:elias.said@unir.net" target="_blank">elias.said@unir.net</a></li><li>This algorithm is related to the hate/non-hate classification algorithm, also developed by the authors: <a href="https://github.com/esaidh266/Algorithm-for-detection-of-hate-speech-in-Spanish" target="_blank">https://github.com/esaidh266/Algorithm-for-detection-of-hate-speech-in-Spanish</a></li><li>This algorithm is related to the algorithm for classifying hate expressions by intensities in Spanish, also developed by the authors: <a href="https://github.com/esaidh266/Algorithm-for-classifying-hate-expressions-by-intensities-in-Spanish" target="_blank">https://github.com/esaidh266/Algorithm-for-classifying-hate-expressions-by-intensities-in-Spanish</a></li></ul><p></p>…”
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77
Contextual Dynamic Pricing with Strategic Buyers
Published 2024“…In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. …”
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78
Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield...
Published 2022“…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”
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79
Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model
Published 2025“…This framework integrates a novel biologically inspired optimization algorithm, the Indian Millipede Optimization Algorithm (IMOA), for effective feature selection. …”
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80
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 2025“…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”