In the era of accelerating growth of genomic data, feature-selection techniques are believed
to become a game changer that can help substantially reduce the complexity of the data …

Feature selection has been an important research area in data mining, which chooses a
subset of relevant features for use in the model building. This paper aims to provide an …

Unplanned readmission (re-hospitalization) is the main source of cost for healthcare
systems and is normally considered as an indicator of healthcare quality and hospital …

Background The ever-increasing availability of high-density genomic markers in the form of
single nucleotide polymorphisms (SNPs) enables genomic prediction, ie the inference of …

What is the simplest thing you can do to solve a problem? In the context of semi-supervised
feature selection, we tackle exactly this—how much we can gain from two simple classifier …

Genomic selection (GS) is a new breeding method that makes use of genome-wide DNA
marker data to improve the efficiency of breeding for quantitative traits. In GS, individuals …

We investigate star-galaxy classification for astronomical surveys in the context of four
methods enabling the interpretation of black-box machine learning systems. The first …

Accurate prediction of complex traits based on whole-genome data is a computational
problem of paramount importance, particularly to plant and animal breeders. However, the …

Background Given a set of biallelic molecular markers, such as SNPs, with genotype values
encoded numerically on a collection of plant, animal or human samples, the goal of genetic …

Feature selection plays an important role in data mining and recognition, especially in the
large scale text, image and biological data. Specifically, the class label information is …