We propose a support vector machine (SVM)-based structural model to forecast the collapse of banking institutions in the USA using publicly disclosed information from their financial statements on a four-year rolling window. In our approach, the optimum input variable set is defined from a large...

Feature selection is an essential pre-processing technique in data mining that eliminates redundant or unrepresentative attributes and improves the performance of classifiers. However, a classifier with different feature selection approaches results in diverse outcomes. Thus, determining how to...

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are...

Optimal student performance is integral for successful higher education institutions. The consensus is that big data analytics can be used to identify ways for achieving better student academic performance. This article used support vector machines to predict future student performance in...

Data description and classification are important tasks in supervised learning. In this study, three supervised learning methods such as k-nearest neighbour (k-NN), support vector data description (SVDD) and support vector machine (SVM) are considered because they do not suffer from the problem...

Bioinformatics is the science of managing, mining and interpreting information from biological sequences and structures. In this paper, we discuss two data-mining techniques that can be applied in bioinformatics: Neural Networks (NN) and Support Vector Machines (SVMs), and their application in...