Recently, feature selection and dimensionality reduction have become fundamental tools for
many data mining tasks, especially for processing high-dimensional data such as gene …

People with hearing impairments are found worldwide; therefore, the development of
effective local level sign language recognition (SLR) tools is essential. We conducted a …

Microbiome data predictive analysis within a machine learning (ML) workflow presents
numerous domain-specific challenges involving preprocessing, feature selection, predictive …

In machine learning, discretization and feature selection (FS) are important techniques for
preprocessing data to improve the performance of an algorithm on high-dimensional data …

The minimun description length (MDL) is a powerful criterion for model selection that is
gaining increasing interest from both theorists and practicioners. It allows for automatic …

Dimensionality reduction is one basic and critical technology for data mining, especially in
current “big data” era. As two different types of methods, feature selection and feature …

High dimensionality of the feature space is one of the major concerns owing to
computational complexity and accuracy consideration in the text clustering. Therefore …

The high-dimensionality of microarray data with small number of samples has presented a
difficult challenge for the microarray data classification task. The aim of gene selection is to …

Due to the ever increasing number of documents in the digital form, automated text
clustering has become a promising method for the text analysis in last few decades. A major …

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health
management (PHM) has led to a performance increase in diagnostics, prognostics, and …