Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

Abstract Systems are becoming autonomous nowadays. In real-world scenarios, the
involvement of experts in generating a new decision has to be paid a certain amount of cost …

Data stream classification is an important research direction in the field of data mining, but in
many practical applications, it is impossible to collect the complete training set at one time …

Online supervised learning from fast-evolving data streams, particularly in domains such as
health, the environment, and manufacturing, is a crucial research area. However, these …

Background Lumbar disc degeneration (LDD) is considered as one of the main causes of
low back pain. For clinical diagnosis of LDD, magnetic resonance imaging (MRI) is …

In this study, we implemented a secure network engine for mobile devices. The study
focused on faster data communication, the authenticity of the user, data transfer reliability …

Data streams are sequences of fast-growing and high-speed data points that typically suffer
from the infinite length, large volume, and specifically unstable data distribution. These …

Online supervised learning from fast-evolving imbalanced data streams has applications in
many areas. That is, the development of techniques that are able to handle highly skewed …

In data mining and machine learning, the class imbalance problem occurs when the number
of samples in one class (minority) is obviously smaller than the other one (majority), ignoring …

鉴于在数据流中无法一次性收集完整的训练集ꎬ 同时数据可能会处于不平衡状态并夹杂概念
漂移而影响分类性能ꎬ 提出一种在线动态集成选择的不平衡漂移数据流Boosting 分类算法ꎮ …