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

The development of new molecular and cell technologies is having a significant impact on
the quantity of data generated nowadays. The growth of omics databases is creating a …

In predictive model development, gene expression data is associated with the unique
challenge that the number of samples (n) is much smaller than the amount of features (p) …

One of the central tasks of genome research is to predict phenotypes and discover some
important gene biomarkers. However, there are three main problems in analyzing genomics …

Feature selection is important in both machine learning and pattern recognition.
Successfully selecting informative features can significantly increase learning accuracy and …

Feature selection is an important research area for big data analysis. In recent years, various
feature selection approaches have been developed, which can be divided into four …

An important application of DNA microarray data is cancer classification. Because of the
high-dimensionality problem of microarray data, gene selection approaches are often …

In recent studies on sparse modeling, the nonconvex regularization approaches
(particularly, L q regularization with q∈(0, 1)) have been demonstrated to possess capability …

The success of deep learning relies heavily on large and diverse datasets, but the storage,
preprocessing, and training of such data present significant challenges. To address these …

Noisy time series prediction is a hot research topic in practical applications. Echo state
networks (ESNs) have superior performance on time series prediction. However, the ill …