In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …

Dominance-based rough approximation discovers inconsistencies from ordered criteria and
satisfies the requirement of the dominance principle between single-valued domains of …

Gene expression data have become increasingly important in machine learning and
computational biology over the past few years. In the field of gene expression analysis …

Nowadays, microarray data processing is one of the most important applications in
molecular biology for cancer diagnosis. A major task in microarray data processing is gene …

In recent decades, the improvement of computer technology has increased the growth of
high-dimensional microarray data. Thus, data mining methods for DNA microarray data …

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

The interaction between features can provide essential information that affects the
performances of learning models. Nevertheless, most feature selection methods do not take …

Neighborhood rough set is a useful mathematic tool to describe uncertainty in mixed data.
Feature selection based on neighborhood rough set has been studied widely. However …

Incremental learning strategy based feature selection approaches can improve the efficiency
of reduction algorithm used for datasets with dynamic characteristic, which has attracted …

Multi-label learning deals with cases where every instance corresponds to multiple labels.
The objective is to learn mapping from an instance to a relevant label set. Existing multi …