In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the
shortage of data monitoring devices and the difficulty of comprehensive coverage of …

This review focuses on recent and potential advances in chemometric methods in relation to
data processing in metabolomics, especially for data generated from mass spectrometric …

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 …

Feature selection plays an important role in data mining and pattern recognition, especially
for large scale data. During past years, various metrics have been proposed to measure the …

Feature selection is a central problem in machine learning and pattern recognition. On large
datasets (in terms of dimension and/or number of instances), using search-based or wrapper …

Spam is commonly defined as unwanted e-mails and it became a global threat against e-
mail users. Although, Support Vector Machine (SVM) has been commonly used in e-mail …

A lot of candidate features are usually provided to a learning algorithm for producing a
complete characterization of the classification task. However, it is often the case that majority …

In modern industry, milling is an important tool when a high material removal rate is
required. Chatter detection in this situation is a crucial step for improving surface quality and …

A new improved forward floating selection (IFFS) algorithm for selecting a subset of features
is presented. Our proposed algorithm improves the state-of-the-art sequential forward …

Rice phenology information is critical for farm management and productivity evaluation.
Synthetic aperture radar (SAR) and optical remote sensing data are very useful for …