Metabolomics experiments usually result in a large quantity of data. Univariate and
multivariate analysis techniques are routinely used to extract relevant information from the …

The research aims to propose the new ensemble models by combining the machine
learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k …

The application of machine learning and artificial intelligence to high‐dimensional cytometry
data sets has increasingly become a staple of bioinformatic data analysis over the past …

Sparse discriminant methods based on independence rules, such as the nearest shrunken
centroids classifier (Tibshirani et al., 2002) and features annealed independence rules (Fan …

For high dimensional classification, it is well known that naively performing the Fisher
discriminant rule leads to poor results due to diverging spectra and accumulation of noise …

The study of the sensitivity and the specificity of a classification test constitute a powerful
kind of analysis since it provides specialists with very detailed information useful for cancer …

Centroid-based classification is a machine learning approach used in the text classification
domain. The main advantage of centroid-based classifiers is their high performance during …

We propose a high dimensional classification method, named the Copula Discriminant
Analysis (CODA). The CODA generalizes the normal-based linear discriminant analysis to …

Motivation: Discriminant analysis for high-dimensional and low-sample-sized data has
become a hot research topic in bioinformatics, mainly motivated by its importance and …

As a well-established feature selection algorithm, principal component analysis (PCA) is
often combined with the state-of-the-art classification algorithms to identify cancer molecular …