Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …

Feature selection is a crucial preprocessing step in data analytics and machine learning.
Classical feature selection algorithms select features based on the correlations between …

We present a comprehensive experimental study of 12 individual as well as 6 ensemble
methods for feature selection for classification tasks on environmental data, more specifically …

Hierarchical classification learning, which organizes data categories into a hierarchical
structure, is an effective approach for large-scale classification tasks. The high …

Visual representation learning is ubiquitous in various real-world applications, including
visual comprehension, video understanding, multi-modal analysis, human-computer …

Recent years have witnessed the proliferation of techniques for streaming data mining to
meet the demands of many real-time systems, where high-dimensional streaming data are …

The real-world recommender system needs to be regularly retrained to keep with the new
data. In this work, we consider how to efficiently retrain graph convolution network (GCN) …

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 …

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 …

In many fields of scientific research and real-world applications, unbiased estimation of
causal effects from non-experimental data is crucial for understanding the mechanism …