In the era of big data, both the size and the number of features, samples, and classes
continue to increase, resulting in high-dimensional classification tasks. One characteristic …

In the age of big data, the number of class labels is increasing rapidly and there exists a
hierarchical structure between different class labels. In the present paper, we revisit the …

The classification of high-dimensional tasks remains a significant challenge for machine
learning algorithms. Feature selection is considered to be an indispensable preprocessing …

With increases in feature dimensions and the emergence of hierarchical class structures,
hierarchical feature selection has become an important data preprocessing step in machine …

Feature selection is an important preprocessing step for high-dimensional data mining and
machine learning; it is viewed as the selection of the optimal granularity to describe the …

Hierarchical classification identifies a sample from the root node to a leaf node along the
hierarchical structures of labels. It is often difficult to perform leaf-node prediction owing to …

Incremental feature selection can improve learning of accumulated data. We focus on
incremental feature selection based on rough sets, which along with their generalizations …

In real-applications, there may exist many kinds of data (eg, boolean, categorical, real-
valued and set-valued data) and missing data in an information system which is called as a …

In recent years, many online streaming feature selection approaches focus on flat data,
which means that all data are taken as a whole. However, in the era of big data, not only the …

Incremental feature selection approaches can improve the efficiency of feature selection
used for dynamic datasets, which has attracted increasing research attention. Nevertheless …