Feature selection (FS) in data with class imbalance or missing values has received much
attention from researchers due to their universality in real-world applications. However, for …

Cancer is a deadly disease which requires a very complex and costly treatment. Microarray
data classification plays an important role in cancer treatment. An efficient gene selection …

Feature selection, aiming at eliminating irrelevant and redundant features, is an important
data preprocessing technology for downstream tasks, eg, classification. With the explosive …

Multilabel learning is a challenging task demanding scalable methods for large-scale data.
Feature selection has shown to improve multilabel accuracy while defying the curse of …

Context Biomarkers that can accurately predict risk of type 1 diabetes (T1D) in genetically
predisposed children can facilitate interventions to delay or prevent the disease. Objective …

Most state-of-the-art feature selection methods tend to overlook the structural relationship
between a pair of samples associated with each feature dimension, which may encapsulate …

Hierarchical Classification is widely used in many real-world applications, where the label
space is exhibited as a tree or a Directed Acyclic Graph (DAG) and each label has rich …

The integration of WiFi fingerprint-based indoor positioning technology and big data
analysis emerges as a new research prospect. Through the analysis of big data collected …

Stochastic multiarmed bandits (stochastic MABs) are a problem of sequential decision-
making with noisy rewards, where an agent sequentially chooses actions under unknown …

For Intrusion Detection Systems (IDSs) in Vehicular Ad Hoc Networks (VANETs), single-
objective optimization algorithm has inherited limitations for the feature selection problem …