In recent years, an enormous amount of research has been carried out on support vector
machines (SVMs) and their application in several fields of science. SVMs are one of the …

Unbalanced classification is an essential machine learning task, which has attracted
widespread attention from both the academic and industrial communities due mainly to its …

Imbalanced data with a skewed class distribution are common in many real-world
applications. Deep Belief Network (DBN) is a machine learning technique that is effective in …

Support vector machines (SVMs) is a popular machine learning technique, which works
effectively with balanced datasets. However, when it comes to imbalanced datasets, SVMs …

Class imbalance problems, where the number of samples in each class is unequal, is
prevalent in numerous real world machine learning applications. Traditional methods which …

Over the last years, Support Vector Machines (SVMs) have become a successful approach
in classification problems. However, the performance of SVMs is affected harshly by skewed …

In this paper, a new weighted approach on Lagrangian support vector machine for
imbalanced data classification problem is proposed. The weight parameters are embedded …

SNPs (Single Nucleotide Polymorphisms) include millions of changes in human genome,
and therefore, are promising tools for disease-gene association studies. However, this kind …

Fungal pathogens have evolved the ability to cause serious plant diseases and threaten the
world food security. Fungal effectors are proteins that exploit the host cellular functions to …

The standard support vector machine (SVM) models are widely used in various fields, but
we show that they are not rationally defined from the perspective of geometric point, which is …