Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …

One of the most promising fields in medicine is the application of artificial intelligence
methods to medical imaging. Though annotating medical images is an expensive operation …

Novelty detection in data streams is the task of detecting concepts that were not known prior,
in streams of data. Many machine learning algorithms have been proposed to detect these …

Recently, broad learning system (BLS) has been introduced to solve industrial fault
diagnosis problems and has achieved impressive performance. As a flat network, BLS …

Brain networks provide essential insights into the diagnosis of functional brain disorders,
such as Alzheimer's disease (AD). Many machine learning methods have been applied to …

Clustering analysis is an important data mining method for data stream. Data stream
clustering is a branch of clustering in which the patterns are processed in an ordered …

In the text mining area, prevalent deep learning models primarily focus on mapping input
features to result of predicted outputs, which exhibit a deficiency in self-dialectical thinking …

Most data stream ensemble classification algorithms use supervised learning. This method
needs to use a large number of labeled data to train the classifier, and the cost of obtaining …

在监督或半监督学习的条件下对数据流集成分类进行研究是一个很有意义的方向. 从基分类器,
关键技术, 集成策略等三个方面进行介绍, 其中, 基分类器主要介绍了决策树, 神经网络 …

Data streams are potentially large and thus data stream classification tasks are not strictly
stationary. In the process of data analysis, the fundamental structure may vary over time and …