Emotion recognition based on electroencephalography (EEG) signals has emerged as a
prominent research field, facilitating objective evaluation of diseases like depression and …

Feature selection has attracted a lot of attention in obtaining discriminative and non-
redundant features from high-dimension data. Compared with traditional filter and wrapper …

Most of the existing unsupervised feature selection methods learn the cluster structure
through spectral clustering, and then use various regression models to introduce the data …

Feature selection can help to reduce data redundancy and improve algorithm performance
in actual tasks. Most of the embedded feature selection models are constructed based on …

A common drawback of EEG-based emotion recognition is that volume conduction effects of
the human head introduce interchannel dependence and result in highly correlated …

Due to the small size of valid samples, multi-source EEG features with high dimensionality
can easily cause problems such as overfitting and poor real-time performance of the …

In this paper, we propose a novel embedded feature selection method, which is able to
select the informative and discriminative features with the underlying submanifolds of data in …

Feature selection is a complicated multi-objective optimization problem with aims at
reaching to the best subset of features while remaining a high accuracy in the field of …

Feature engineering is the core problem in pattern recognition of electroencephalogram
signals, which directly affects the design and performance of classifiers. Feature engineering …

Microarray-generated genomic data has recently sparked a wave of bioinformatics and data
mining research. However, such data presents significant challenges for further analysis due …