In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the
shortage of data monitoring devices and the difficulty of comprehensive coverage of …

In real world, decision-makers' regret psychology often affects decision outcomes due to
uncertain risks. Moreover, decision information may be missing in the process of data …

Abstract Concept-cognitive learning (CCL) is an emerging field for studying the
representation and processing of knowledge embedded in data. Many efforts are focused on …

In many real-world multi-label applications, the content of multi-label data is usually
characterized by high dimensional features, which contains complex correlation information …

Medical decision-making with high-dimensional complex data has recently become a focus
and difficulty in artificial intelligence and the medical field. Tumor diagnosis using data …

Multilabel data contains rich label semantic information, and its data structure conforms to
the cognitive laws of the actual world. However, these data usually involve many irrelevant …

Feature selection aims to remove irrelevant or redundant features and thereby remain
relevant or informative features so that it is often preferred for alleviating the dimensionality …

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

Data reduction, aiming to reduce the original data by selecting the most representative
information, is an important technique of preprocessing data. At present, large-scale or huge …

Label distribution learning is a widely studied supervised learning diagram that can handle
the problem of label ambiguity. The increasing size of datasets is accompanied by the …