Benchmarking prognosis methods for survivability – A case study for patients with contingent primary cancers

Makond, Bunjira; Wang, Kung‐Jeng; Wang, Kung-Min

doi:10.1016/j.compbiomed.2021.104888

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…The Synthetic Minority Over-sampling Technique (SMOTE) was designed to generate new instances from minority classes by interpolating the instances that are close to each other [10]. As such, it has been used in different works, such as in a real-time traffic and weather data based on crash prediction, by generating crash events that tend to be under-represented [14], and to address an imbalanced problem in the context of a fiveyear survival prognosis prediction, obtaining significant improvements in both sensitivity and specificity [15]. Different modifications of SMOTE have been proposed in order to improve the quality of the generated data.…”

Section: Data Augmentationmentioning

confidence: 99%

Exploring Data Augmentation and Active Learning Benefits in Imbalanced Datasets

Moles,

Andres,

Echegaray

et al. 2024

Mathematics

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Despite the increasing availability of vast amounts of data, the challenge of acquiring labeled data persists. This issue is particularly serious in supervised learning scenarios, where labeled data are essential for model training. In addition, the rapid growth in data required by cutting-edge technologies such as deep learning makes the task of labeling large datasets impractical. Active learning methods offer a powerful solution by iteratively selecting the most informative unlabeled instances, thereby reducing the amount of labeled data required. However, active learning faces some limitations with imbalanced datasets, where majority class over-representation can bias sample selection. To address this, combining active learning with data augmentation techniques emerges as a promising strategy. Nonetheless, the best way to combine these techniques is not yet clear. Our research addresses this question by analyzing the effectiveness of combining both active learning and data augmentation techniques under different scenarios. Moreover, we focus on improving the generalization capabilities for minority classes, which tend to be overshadowed by the improvement seen in majority classes. For this purpose, we generate synthetic data using multiple data augmentation methods and evaluate the results considering two active learning strategies across three imbalanced datasets. Our study shows that data augmentation enhances prediction accuracy for minority classes, with approaches based on CTGANs obtaining improvements of nearly 50% in some cases. Moreover, we show that combining data augmentation techniques with active learning can reduce the amount of real data required.

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Section: Data Augmentationmentioning

confidence: 99%