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 …

Label distribution learning (LDL) is a novel framework for handling label ambiguity problems
and has been used widely in practice. However, dealing with high-dimensional data or data …

The relative decision self-information is a crucial evaluation function of feature selection in
information system. It encapsulates classification information in upper and lower …

Fuzzy rough set theory can model uncertainty in data and has been applied to feature
selection for machine learning tasks. The existence of noise in data is one of the reasons for …

Dimensionality reduction is considered in many learning methods using discriminative
features to obtain optimal performance. In general, feature extraction and feature selection …

Early intervention and prevention of chronic diseases is a fundamental way to reduce
disease incidence. Due to the complex pathogenic factors of disease, the assessment of …

Multi-objective clustering algorithms (MOCAs) are popular in unsupervised image
segmentation due to their merit of meeting multiple segmentation requirements and the …

The multi-scale decision information system (MDIS) is a typical granular computing model. In
the research of MDIS, uncertainty is an important factor in making decision analysis, and the …

Feature extraction is a fundamental and challenging task in machine learning, which aims at
extracting a subset of significant and discriminant features from raw data for various …

Due to the diversity and complexity of the actual data distribution, the traditional
neighborhood classifier (NEC) is weak in adapting to the global data and has low utilization …