Industrial products typically lack defects in smart manufacturing systems, which leads to an
extremely imbalanced task of recognizing surface defects. With this imbalanced sample …

Cluster ensembles have been shown to be better than any standard clustering algorithm at
improving accuracy and robustness across different data collections. This meta-learning …

In this paper, our goal is to design a simple learning paradigm for long-tail visual
recognition, which not only improves the robustness of the feature extractor but also …

The problem of imbalanced data classification often exists in medical diagnosis. Traditional
classification algorithms usually assume that the number of samples in each class is similar …

Stacked autoencoders (SAEs), as part of the deep learning (DL) framework, have been
recently proposed for feature extraction in hyperspectral remote sensing. With the help of …

Maritime safety is crucial for international seaborne trade and the global economy.
Acknowledging the inevitable and multifaceted risks present in maritime navigation, we …

Multi-label classification has attracted increasing attention in various applications, such as
medical diagnosis and semantic annotation. With such trend, a large number of ensemble …

Medical image classification is an important task in computer-aided diagnosis systems. Its
performance is critically determined by the descriptiveness and discriminative power of …

The classification on imbalanced data sets is a great challenge in machine learning. In this
paper, a geometric structural ensemble (GSE) learning framework is proposed to address …

Cluster ensemble is an important research content of ensemble learning, which is used to
aggregate several base clusterings to generate a single output clustering with improved …