Abstract: AdaBoost approaches have been used for multi-class imbalance classification with an imbalance ratio measured on class sizes. However, such ratio would assign each training sample of the same ...

GRSE laid the keel for the second Multi-Purpose Vessel for Germany's Carsten Rehder, part of its largest-ever MPV contract.

Abstract: Multi-label image classification is an essential yet challenging task that requires to recognize multiple objects of images. To this end, recent studies have sought to acquire visual ...

Environmental and chemical exposures have emerged as critical contributors to the global cancer burden. Sources such as dietary contaminants, food ...

Dulles Krishnan of Avalara shares how early automation simplifies compliance, transforming it from a growth risk into a ...

Dietary assessment has long been a bottleneck in nutrition research and public health. Common tools such as food frequency questionnaires, 24-hour recalls, and weighed food records rely heavily on ...

Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image ...

I'm working in a hierarchical multi class problem, and if I flat the labels (flat approach) I have about 1193 classes, which perhaps can already be consider a extreme multi classification problem.

Dr. James McCaffrey of Microsoft Research provides a full-code, step-by-step machine learning tutorial on how to use the LightGBM system to perform multi-class classification using Python and the ...