As semi-supervised feature selection is becoming much more popular among researchers,
many related methods have been proposed in recent years. However, many of these …

Feature selection has become a mandatory task in data mining, due to the overwhelming
amount of features in Big Data problems. To handle this high-dimensional data and avoid …

The ongoing data boom has democratized the use of data for improved decision-making.
Beyond gathering voluminous data, preprocessing the data is crucial to ensure that their …

Biological data is prone to grow exponentially, which consumes more resources, time and
manpower. Parallelization of algorithms could reduce overall execution time. There are two …

The big data concept has elicited studies on how to accurately and efficiently extract
valuable information from such huge dataset. The major problem during big data mining is …

Extreme-scale computing involves hundreds of millions of threads with multi-level
parallelism running on large-scale hierarchical and heterogeneous hardware. Some …

Feature selection is a subfield of machine learning focused on reducing the dimensionality
of datasets by performing a computationally intensive process. This work presents Parallel …

Feature selection algorithms are necessary nowadays for machine learning as they are
capable of removing irrelevant and redundant information to reduce the dimensionality of …

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 …

In a world where the volume of data is constantly increasing, the implementation time of
various processes increases significantly. Therefore, the proper management and the effort …