Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for
durable electric vehicles. Early detection of inadequate performance facilitates timely …

In the era of big data, all types of data with increasing samples and high-dimensional
attributes are demonstrating their important roles in various fields, such as data mining …

Causal feature selection has attracted much attention in recent years, since it has better
robustness than the traditional feature selection. Existing causal feature selection algorithms …

Causal feature selection has achieved much attention in recent years, which discovers a
Markov boundary (MB) of the class attribute. The MB of the class attribute implies local …

Data heterogeneity is one of the big challenges in modern data analysis caused by the
effects of unknown/unwanted factors introduced during data collection procedures. It will …

High-dimensional data in many machine learning applications leads to computational and
analytical complexities. Feature selection provides an effective way for solving these …

The applications of traditional statistical feature selection methods to high-dimension, low-
sample-size data often struggle and encounter challenging problems, such as overfitting …

With the increasing data dimensionality, feature selection has become a fundamental task to
deal with high-dimensional data. Semi-supervised feature selection focuses on the problem …

Relevance vector regression (RVR) is a useful tool for degradation modeling and remaining
useful life (RUL) prediction. However, most RVR models are for 1-D degradation processes …

Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose
a subset of relevant samples from the input data and make reliable probabilistic predictions …