This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for
the Feature Selection (FS) problem. FS is an essential machine learning and data mining …

Feature selection has been studied by many researchers using information theory to select
the most informative features. Up to now, however, little attention has been paid to the …

Feature selection, as a dimension reduction technique in data mining and pattern
recognition, aims to select the most discriminative features and improve the learning …

Graph-based sparse feature selection plays an important role in semi-supervised feature
selection. However, traditional graph-based semi-supervised sparse feature selection …

Plant nitrogen content (PNC) is vital for evaluating crop nitrogen nutrient status and for net
primary productivity. Therefore, rapid and accurate acquisition of crop PNC information can …

Current machine learning algorithms encounter challenges such as missing labels and high
dimensionality. Feature selection serves as an effective dimensionality reduction technique …

As a typical multigranularity data analysis model, multi-scale rough sets have attracted
considerable attention in recent years. However, classical multi-scale rough sets and most of …

In this paper, we propose a new feature selection approach with partially labeled training
examples in the multi-class classification setting. It is based on a new modification of the …

Partial labeled data is ubiquitous in the big data era. Selecting informative features, and
avoiding redundant and noise features is an important task for constructing robust learning …

Small target features are difficult to distinguish and identify in an environment with complex
backgrounds. The identification and extraction of multi-dimensional features have been …