Abstract: Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes. In the literature, there exists a distinction between hard and soft classification. In soft classification, the conditional class probability is modeled as a function of the covariates. In contrast, hard c...

The purpose of this paper is to study soft sets and soft topological spaces. We also introduce the concepts of soft interior point, soft interior, soft neighborhood, soft continuity and soft compactness. The relationships between soft topology and fuzzy topology are also provided. Also, some basic properties of these concepts are explored. 2010 AMS Classification: 06D72, 54A40

Neonatal jaundice or hyperbilirubinemia and its evolution to acute bilirubin encephalopathy (ABE) and kernicterus are an important, yet avoidable, origin of newborn deaths, re-hospitalisations and disabilities generally. In this study, a new supervised hybrid bijective soft set neural network-based classification method is introduced for prediction of Egyptian neonatal jaundice dataset. Early p...

Binary neural networks leverage $\mathrm{Sign}$ function to binarize weights and activations, which require gradient estimators overcome its non-differentiability will inevitably bring errors during backpropagation. Although many hand-designed soft functions have been proposed as better approximate gradients, their mechanism is not clear there are still huge performance gaps between binary mode...

In this article, the authors present a hybrid approach to produce more accurate land cover maps of diverse landscapes representing Mediterranean environments. The innovation of the proposed methodology is to use, in the combination of soft classifiers outputs, information about the classification uncertainty associated with each pixel and its neighbourhood. The hybrid combined classification me...