Datasets comprise a collection of features; however, not all of these features may be
necessary. Feature selection is the process of identifying the most relevant features while …

Data classification is the most common task in machine learning, and feature selection is the
key step in the classification task. Common feature selection methods mainly analyze the …

Currently, feature selection faces a huge challenge that no single feature selection method
can effectively deal with various data sets for all real cases. Ensemble learning is a potential …

Graph contrastive learning (GCL) enhances unsupervised graph representation by
generating different contrastive views, in which properties of augmented nodes are required …

Gene selection is a process of selecting discriminative genes from microarray data that
helps to diagnose and classify cancer samples effectively. Swarm intelligence evolution …

Rotating machinery, such as ventilators and water pumps, are crucial components in
modern industry, of which safety monitoring requires intelligent fault diagnosis. Feature …

With the continuous evolution of artificial intelligence and sensor technology, there is a
growing accumulation of unlabeled data. Uncovering valuable insights from this data has …

Abstract In feature selection, Markov Blanket (MB) based approaches have attracted
considerable attention with most MB discovery algorithms being categorized as filter based …

Dampak pandemi COVID-19 membuat lembaga pendidikan mengubah metode belajar
menjadi pembelajaran jarak jauh secara daring. Banyak perguruan tinggi menyatakan …

The neighborhood information theory have been used to evaluate the relevance and
redundancy in feature selection for mixed data containing discrete and continuous features …