This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

Abstract Support Vector Machines (SVM) are an efficient alternative for supervised
classification. In the soft margin SVM model, two different objectives are optimized and the …

Underground water pipes deteriorate under the influence of various physical, mechanical,
environmental, and social factors. Reliable pipe failure prediction is essential for a proactive …

About half of the people who develop heart failure (HF) die within five years of diagnosis.
Over the years, researchers have developed several machine learning-based models for the …

In many big-data systems, large amounts of information are recorded and stored for
analytics purposes. Often however, this vast amount of information does not offer additional …

Background: The bootstrap can be alternative to cross-validation as a training/test set
splitting method since it minimizes the computing time in classification problems in …

Accurate diagnosis and treatment of cardiovascular diseases require the integration of
cardiac imaging, which provides crucial information about the structure and function of the …

Automatic heart disease prediction is a major global health concern. Effective cardiac
treatment requires an accurate heart disease prognosis. Therefore, this paper proposes a …

Companies in charge of water supply networks are making a huge effort to optimally plan
the annual replacements of pipes. This would save costs, enable a higher quality of service …

In this work we propose two formulations based on Support Vector Machines for
simultaneous classification and feature selection that explicitly incorporate attribute …