Abstract: Class imbalanced classification presents a considerable difficulty in machine learning, as conventional algorithms typically exhibit bias towards the majority class, compromising minority ...

ABSTRACT: From the perspective of student consumption behavior, a data-driven framework for screening student loan eligibility was developed using K-means clustering analysis and decision tree models.

The authors investigate a quantum support vector algorithm that uses qudits to identify the most accurate way of solving a prototype machine learning task: the binary classification of point clusters.

Jaime Tugayev is the News Editor for DualShockers with over a decade of experience, and a much longer love for fantasy, shooters and strategy games. Show me the facts Explain it like I’m 5 Give me a ...

Marc Santos is a Guides Staff Writer from the Philippines with a BA in Communication Arts and over six years of experience in writing gaming news and guides. He plays just about everything, from ...

Alterations in brain structure have been suggested to be associated with bulimia nervosa (BN). This study aimed to employ machine learning (ML) methods based on diffusion tensor imaging (DTI) to ...

Abstract: Classification tasks have long been a central concern in the field of machine learning. Although deep neural network-based approaches offer a novel, versatile, and highly precise solution ...

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.

In the era of big data and artificial intelligence, machine learning is one of the hot issues in the field of credit rating. On the basis of combing the literature on credit rating methods at home and ...