In many real-world application domains, eg, text categorization and image annotation,
objects naturally belong to more than one class label, giving rise to the multi-label learning …

Graph-based sparse feature selection plays an important role in semi-supervised feature
selection, which greatly improves the performance of feature selection. However, most …

Glaucoma is the second most common cause of vision loss. Manual screening of a patient's
eye or screening through a fundus image of the patient's eye requires expert …

High-dimensional datasets depict a challenge for learning tasks in data mining and machine
learning. Feature selection is an effective technique in dealing with dimensionality reduction …

Abstract Information theoretic-guided feature selection approaches (ITFSs), which exploit the
uncertainty of information to measure the correlation of features, aim to select the most …

Data mining and machine learning algorithms' performance is degraded by data of high-
dimensional nature due to an issue called “curse of dimensionality”. Feature selection is a …

Glaucoma, commonly known as the silent thief of sight, is the second most common cause of
blindness in humans, and the number of cases is steadily increasing. Conventional …

Stock price forecasting is a challenging task due to the complexity of financial markets and
the high volatility of stocks. Because of the strong nonlinear representation ability of neural …

Feature selection (FS) is crucial to transforming high-dimensional data into low-dimensional
data. The FS approach selects influential traits and ignores the rest. This approach improves …

Monotonic classification is a common task in the field of multicriteria decision-making, in
which features and decision obey a monotonic constraint. The dominance-based rough set …