Real-world problems are commonly characterized by a high feature dimensionality, which
hinders the modelling and descriptive analysis of the data. However, some of these data …

In the era of big data, the dimensionality of data is increasing dramatically in many domains.
To deal with high dimensionality, online feature selection becomes critical in big data …

We propose a new online feature selection framework for applications with streaming
features where the knowledge of the full feature space is unknown in advance. We define …

Due to complex semantics, a sample may be associated with multiple labels in various
classification and recognition tasks. Multilabel learning generates training models to map …

When tackling high dimensionality in data mining, online feature selection which deals with
features flowing in one by one over time, presents more advantages than traditional feature …

Traditional feature selection methods assume that all data instances and features are known
before learning. However, it is not the case in many real-world applications that we are more …

Online streaming feature selection, as a new approach which deals with feature streams in
an online manner, has attracted much attention in recent years and played a critical role in …

Online streaming feature selection which deals with streaming features in an online manner
plays a critical role in big data problems. Many approaches have been proposed to handle …

Abstract Convolutional Neural Networks (CNN) offer promising opportunities to
automatically glean scientifically relevant information directly from annotated images …

Machine learning methods are expected to play a significant role in battery state of charge
(SOH) estimation, leveraging their strengths in self-learning and nonlinear fitting. One of the …