Rooted in statistical learning or Vapnik-Chervonenkis (VC) theory, support vector machines
(SVMs) are well positioned to generalize on yet-to-be-seen data. The SVM concepts …

In this paper, we propose three novel profit-driven strategies for churn prediction. Our
proposals extend the ideas of the Minimax Probability Machine, a robust optimization …

Learning from imbalanced data sets is an important machine learning challenge, especially
in Support Vector Machines (SVM), where the assumption of equal cost of errors is made …

A novel framework for profit-based credit scoring is proposed in this work. The approach is
based on robust optimization, which is designed for dealing with uncertainty in the data, and …

In this paper, we investigate the chance-constrained support vector machine (SVM) problem
in which the data points are virtually uncertain although some properties of distributions are …

In this work, two novel formulations for embedded feature selection are presented. A second-
order cone programming approach for Support Vector Machines is extended by adding a …

A novel binary classification approach is proposed in this paper, extending the ideas behind
nonparallel support vector machine (NPSVM) to robust machine learning. NPSVM …

Second-order cone programming (SOCP) formulations have received increasing attention
as robust optimization schemes for Support Vector Machine (SVM) classification. These …

Binary classification is the problem of predicting the class a given sample belongs to. To
achieve a good prediction performance, it is important to find a suitable model for a given …

In this paper, we consider the link prediction problem, where we are given a partial snapshot
of a network at some time and the goal is to predict the additional links formed at a later time …