Obtaining informative features is crucial in imbalanced classification. However, existing
neighborhood rough set-based feature selection approaches easily overlook the diversity …

Ensemble learning is one of the best solutions for imbalanced classification problems.
Diversity is a key factor that affects the performance of ensemble learning. Most existing …

In this paper, we propose a novel approach (called GPT4MIA) that utilizes Generative Pre-
trained Transformer (GPT) as a plug-and-play transductive inference tool for medical image …

Coal-rock cutting state (CRCS) recognition plays an important role in improving shearer
automation and ensuring the safe and efficient mining of coal mines. However, due to the …

We consider the paradigm of unsupervised anomaly detection, which involves the
identification of anomalies within a dataset in the absence of labeled examples. Though …

Developing effective binary classifiers for imbalanced datasets poses significant challenges,
which are further compounded in the case of multi-class imbalanced datasets. To address …

This paper investigates predictive probability inference for classification tasks using random
forests in the context of imbalanced data. In this setting, we analyze the asymptotic …

(南京理工大学计算机科学与工程学院, 南京, 210094) 摘要: 近些年, 基于深度学习的算法和模型
在各种图像分析任务中都取得了显著的成功, 与常见的自然图像相比, 医学图像数据集依然面临 …

Class imbalance learning (CIL) has become one of the most challenging research topics. In
this article, we propose a Boosted co‐training method to modify the class distribution so that …

Quantum computing is a contemporary engineering discipline that innovatively overcomes
computational burdens. This study applies quantum computing techniques to data analyses …