Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review
AAH de Hond, AM Leeuwenberg, L Hooft… - NPJ digital …, 2022 - nature.com
While the opportunities of ML and AI in healthcare are promising, the growth of complex data-
driven prediction models requires careful quality and applicability assessment before they …
driven prediction models requires careful quality and applicability assessment before they …
On the joint-effect of class imbalance and overlap: a critical review
Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …
other data intrinsic characteristics, among which class overlap stands out as one of the most …
Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation
Advances in microscopy hold great promise for allowing quantitative and precise
measurement of morphological and molecular phenomena at the single-cell level in …
measurement of morphological and molecular phenomena at the single-cell level in …
On the class overlap problem in imbalanced data classification
Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …
existing and recent literature showed that class overlap had a higher negative impact on the …
GHOST: adjusting the decision threshold to handle imbalanced data in machine learning
Machine learning classifiers trained on class imbalanced data are prone to overpredict the
majority class. This leads to a larger misclassification rate for the minority class, which in …
majority class. This leads to a larger misclassification rate for the minority class, which in …
Handling class-imbalance with KNN (neighbourhood) under-sampling for software defect prediction
S Goyal - Artificial Intelligence Review, 2022 - Springer
Abstract Software Defect Prediction (SDP) is highly crucial task in software development
process to forecast about which modules are more prone to errors and faults before the …
process to forecast about which modules are more prone to errors and faults before the …
A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans
Stress is the most prevailing and global psychological condition that inevitably disrupts the
mood and behavior of individuals. Chronic stress may gravely affect the physical, mental …
mood and behavior of individuals. Chronic stress may gravely affect the physical, mental …
Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning
Class imbalance occurs when the class distribution is not equal. Namely, one class is under-
represented (minority class), and the other class has significantly more samples in the data …
represented (minority class), and the other class has significantly more samples in the data …
Classification of imbalanced data: review of methods and applications
Imbalance in dataset enforces numerous challenges to implement data analytic in all
existing real world applications using machine learning. Data imbalance occurs when …
existing real world applications using machine learning. Data imbalance occurs when …