机器学习的求解过程可以看作是在假设空间中搜索一个具有强泛化能力和高鲁棒性的学习模型,
而在假设空间中寻找合适模型的过程是较为困难的. 然而, 集成学习作为一类组合优化的学习 …

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 …

The advent of architectures such as the Internet of Things (IoT) has led to the dramatic
growth of data and the production of big data. Managing this often-unlabeled data is a big …

For emerging industrial Internet of Things (IIoT), intelligent anomaly detection is a key step to
build smart industry. Especially, explosive time-series data pose enormous challenges to the …

Ensemble learning has been shown to be an effective approach to solve multi-label
classification problem. However, most existing ensemble learning methods do not consider …

The process of solving machine learning can be regarded as searching for a learning model
with strong generalization ability and high robustness in the hypothesis space, and it is more …

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 …

Abstract Prescription of Traditional Chinese Medicine (TCM) is a precious treasure
accumulated in the long-term development of TCM. Artificial intelligence (AI) technology is …

In this paper we propose and discuss different Deep Learning-based ensemble algorithms
for a problem of low-visibility events prediction due to fog. Specifically, seven different Deep …