Near-infrared (NIR) spectroscopy has increasingly been adopted as an analytical tool in
various fields, such as the petrochemical, pharmaceutical, environmental, clinical …

Global optimisation and search problems are abundant in science and engineering,
including spectroscopy and its applications. Therefore, it is hardly surprising that general …

A PLS-bootstrap-VIP approach is proposed as a simple wavelength selection method, yet
having the ability to identify relevant spectral intervals. This approach is particularly attractive …

A common problem found in statistics, signal processing, data analysis and image
processing research is the estimation of mutual information, which tends to be difficult. The …

In many signal processing applications, grouping of features during model development and
the selection of a small number of relevant groups can be useful to improve the …

Feature selection is an important preprocessing task for many machine learning and pattern
recognition applications, including regression and classification. Missing data are …

Feature selection is an important preprocessing step for many high-dimensional regression
problems. One of the most common strategies is to select a relevant feature subset based on …

In clinical medicine, multidimensional time series data can be used to find the rules of
disease progress by data mining technology, such as classification and prediction. However …

Mutation analysis is a powerful and unbiased technique to assess the quality of input values
and test oracles. However, its application domain is still limited due to the fact that it is a time …

Spectral data often have a large number of highly-correlated features, making feature
selection both necessary and uneasy. A methodology combining hierarchical constrained …