A Novel Hybrid Sampling Framework for Imbalanced Learning

Newaz, Asif; Haq, Farhan Shahriyar

doi:10.2139/ssrn.4200131

Cited by 1 publication

(2 citation statements)

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“…It leads the model to be skewed towards the majority class, creating bias and rendering the algorithm unable to adapt to the features of the minority classes [23,24]. This imbalance can be treated by undersampling the majority class, and there are numerous methods in the literature in order to do so [27][28][29]. These methods include randomly selecting a subset of the samples in the majority class [30,31], or using model-based methods such as NearMiss, Tomek Links, or Edited Nearest Neighbours [27][28][29].…”

Section: Data Preprocessingmentioning

confidence: 99%

“…This imbalance can be treated by undersampling the majority class, and there are numerous methods in the literature in order to do so [27][28][29]. These methods include randomly selecting a subset of the samples in the majority class [30,31], or using model-based methods such as NearMiss, Tomek Links, or Edited Nearest Neighbours [27][28][29]. NearMiss-2 was found to perform the best, and is used in the sequelae.…”

Section: Data Preprocessingmentioning

confidence: 99%

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Rapid Visual Screening Feature Importance for Seismic Vulnerability Ranking via Machine Learning and SHAP Values

Karampinis,

Iliadis,

Karabinis

2024

Applied Sciences

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Structures inevitably suffer damage after an earthquake, with severity ranging from minimal damage of nonstructural elements to partial or even total collapse, possibly with loss of human lives. Thus, it is essential for engineers to understand the crucial factors that drive a structure towards suffering higher degrees of damage in order for preventative measures to be taken. In the present study, we focus on three well-known damage thresholds: the Collapse Limit State, Ultimate Limit State, and Serviceability Limit State. We analyze the features obtained via Rapid Visual Screening to determine whether or not a given structure crosses these thresholds. To this end, we use machine learning to perform binary classification for each damage threshold, and use explainability to quantify the effect of each parameter via SHAP values (SHapley Additive exPlanations). The quantitative results that we obtain demonstrate the potential applicability of ML methods for recalibrating the computation of structural vulnerability indices using data from recent earthquakes.

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