Classification is an important task in various areas. In many real-world applications, class
imbalance and overlapping problems have been reported as major issues in the application …

The class imbalance problem is a pervasive issue in many real-world domains.
Oversampling methods that inflate the rare class by generating synthetic data are amongst …

The class imbalance problem is a pervasive issue in many real-world domains.
Oversampling methods that inflate the rare class by generating synthetic data are amongst …

In this paper, we investigate the impact of imbalanced data on the convergence of
distributed dual coordinate ascent in a tree network for solving an empirical loss …

A polar projection-based algorithm is proposed to reduce the computational complexity
associated with dimension reduction in unsupervised learning. This algorithm employs K …

The continued proliferation of location-based social media and ridesharing services brings
up the omnipresence of geo-textual data, which is often characterized by high arrival rates …

Training a classification model on a dataset where the instances of one class outnumber
those of the other class is a challenging problem. Such imbalanced datasets are standard in …

Large-scale rare events data are commonly encountered in practice. To tackle the massive
rare events data, we propose a novel distributed estimation method for logistic regression in …

The distributed alternating direction method of multipliers (ADMM) is one of the most widely
used algorithms to solve large-scale optimization problems. Since the memory consumption …

Class imbalance is commonly observed in real-world data, and it is problematic in that it
degrades classification performance due to biased supervision. Undersampling is an …