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 …

Unsupervised anomaly detection is an important research direction in the process of
unsupervised knowledge acquisition. It has been successfully applied in many fields, such …

Information theoretical-based methods have attracted a great attention in recent years and
gained promising results for multilabel feature selection (MLFS). Nevertheless, most of the …

Most existing imbalanced data classification models mainly focus on the classification
performance of majority class samples, and many clustering algorithms need to manually …

Multi-label feature selection is an indispensable technology in multi-semantic high-
dimensional data preprocessing, which has been brought into focus in recent years …

Feature selection has aroused extensive attention and aims at selecting features that are
highly relevant to classification from raw datasets to improve the performance of a learning …

The main challenge in extracting coastal aquaculture ponds is how to weaken the influence
of the “same-spectrum foreign objects” effect and how to improve the definition of the …

For multilabel classification, the correlations among labels of samples are always ignored by
existing feature selection models, which results in inefficient predictions. In addition, the …

High-dimensionality is the most noticeable characteristic of multilabel data. In practice,
multilabel data typically contain complex noises. Ignoring these noises in the feature …

Crawler detection is always an important research topic in network security. With the
development of web technology, crawlers are constantly updating and changing, and their …