Classification performance (accuracy, sensitivity, specificity, F1-score, and precision) based on 50 selected features, reported as over 500 runs.

Classification performance (accuracy, sensitivity, specificity, F1-score, and precision) based on 50 selected features, reported as over 500 runs.

<p>Classification performance (accuracy, sensitivity, specificity, F1-score, and precision) based on 50 selected features, reported as over 500 runs.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Sheema Gul (21519289) (author)
مؤلفون آخرون:	Dost Muhammad Khan (3368810) (author), Saeed Aldahmani (15353913) (author), Zardad Khan (3368813) (author)
منشور في:	2025
الموضوعات:	Cell Biology Genetics Immunology Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> high nearest neighbors classifiers margin weights extracted e ., accuracy amplification factor along feature ranking process using random forest support vector machines paper also gives minority amplification factor proportional overlap score proposed method outperforms features thereby highlighting ensemble minimum redundancy discard redundant genes minority class observation feature selection methods robust weighted score feature selection proposed method support vectors robust solution constrained using also generated existing methods conventional methods class separation class imbalance fisher score wilcoxon rank weighted signal unbalanced data sum test study proposes rds integrates ranked genes noise ratio maximum relevance key challenges k </ futher assess discriminative power deeper understanding binary classification
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

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