بدائل البحث:
robust classification » forest classification (توسيع البحث), risk classification (توسيع البحث), group classification (توسيع البحث)
based optimization » whale optimization (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
binary b » binary _ (توسيع البحث)
b based » _ based (توسيع البحث), 1 based (توسيع البحث), 2 based (توسيع البحث)
robust classification » forest classification (توسيع البحث), risk classification (توسيع البحث), group classification (توسيع البحث)
based optimization » whale optimization (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
binary b » binary _ (توسيع البحث)
b based » _ based (توسيع البحث), 1 based (توسيع البحث), 2 based (توسيع البحث)
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Individual Transition Label Noise Logistic Regression in Binary Classification for Incorrectly Labeled Data
منشور في 2021"…<p>We consider a binary classification problem in the case where some observations in the training data are incorrectly labeled. …"
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The Pseudo-Code of the IRBMO Algorithm.
منشور في 2025"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"
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IRBMO vs. meta-heuristic algorithms boxplot.
منشور في 2025"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"
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IRBMO vs. feature selection algorithm boxplot.
منشور في 2025"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"
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