Contributed by Emmanuel J. Candès, September 19, 2018 (sent for review June 21, 2018; reviewed by Nancy M. Reid and Huibin [Harry] Zhou) ...

Abstract: Because of the strong convexity and probabilistic underpinnings, logistic regression (LR) is widely used in many real-world applications. However, in many problems, such as bioinformatics, ...

Abstract: Feature selection is a pivotal step in machine learning, aimed at reducing feature dimensionality and improving model performance. Conventional feature selection methods, typically framed as ...

Study objective: In social epidemiology, it is easy to compute and interpret measures of variation in multilevel linear regression, but technical difficulties exist in the case of logistic regression.

In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...

High-throughput sequencing of 16S gene or metagenomes provides an unprecedented opportunity to discover microbes associated with traits such as clinical outcomes or environmental factors. However, the ...

When predicting financial distress, an imbalanced data set of company data may cause overfitting to the majority class and lead to bad performance of the classifiers. The problem of classification ...