In recent times, big data classification has become a hot research topic in various domains,
such as healthcare, e-commerce, finance, etc. The inclusion of the feature selection process …

By identifying relevant features from the original data, feature selection methods can
maintain or improve the classification accuracy and reduce the dimensionality. Recently …

Feature selection is a crucial preprocessing technique in data mining and machine learning
and has attracted increasing attentions. However, the relevance of existing methods only …

In this data-driven era, where a large number of attributes are often publicly available,
redundancy becomes a major problem, which leads to large storage and computational …

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 …

Feature reduction is essential at the preprocessing stage of designing any reliable and fast
disease diagnosis model. Addressing the limitations like disease specificity, information …

As the feature dimension of data continues to expand, the task of selecting an optimal subset
of features from a pool of limited labeled data and extensive unlabeled data becomes more …

This paper aims to propose and theoretically analyze a new distributed scheme for sparse
linear regression and feature selection. The primary goal is to learn the few causal features …

The remarkable development in technology has led to the increase of massive big data.
Machine learning processes provide a way for investigators to examine and particularly …

The class-imbalance problem emerges when the class labels of a dataset have a skewed
distribution. In this circumstance, the instances belonging to one class, which is exactly the …