The rapid development of precision medicine in recent years has started to challenge
diagnostic pathology with respect to its ability to analyze histological images and …

With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

Current learning machines have successfully solved hard application problems, reaching
high accuracy and displaying seemingly intelligent behavior. Here we apply recent …

Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

We summarise various ways of performing dimensionality reduction on high‐dimensional
microarray data. Many different feature selection and feature extraction methods exist and …

Motivation: The accurate prediction of residue–residue contacts, critical for maintaining the
native fold of a protein, remains an open problem in the field of structural bioinformatics …

We're surrounded by huge amounts of large-scale high-dimensional data, but learning tasks
require reduced data dimensionality. Feature selection has shown its effectiveness in many …

We design sparse and block sparse feedback gains that minimize the variance amplification
(ie, the H 2 norm) of distributed systems. Our approach consists of two steps. First, we …

Motivation: Classification and feature selection of genomics or transcriptomics data is often
hampered by the large number of features as compared with the small number of samples …

This volume examines modern techniques and research problems in the analysis of lifetime
data analysis. This area of statistics deals with time-to-event data which is complicated not …