Biologists are confronting a problem they thought they had mostly solved: what, exactly, counts as life. A wave of ...

Artificial intelligence brings to classification a scalable, accurate alternative. Using natural language processing and ...

Abstract: Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, ...

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.

If you want to accentuate the importance of a problem, it seems sensible to explain how prevalent it is. Lots of people are at risk of Alzheimer’s disease. Lots of women carry a gene that makes them ...

The International Classification of Diseases, or ICD, is a classification system for all physical and mental diseases produced by the World Health Organization (WHO). It’s used for diagnosis, research ...

Abstract: The classification problem concerning crisp-valued data has been well resolved. However, interval-valued data, where all of the observations’ features are described by intervals, are also a ...

Dr. James McCaffrey from Microsoft Research presents a full-code, step-by-step tutorial on using the LightGBM tree-based system to perform binary classification (predicting a discrete variable that ...

Twenty-six years ago, a bipartisan Senate commission chaired by late Senator Daniel Moynihan warned that excessive government secrecy and overclassification would have significant consequences for the ...