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Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for our upcoming Special Issue, "Machine Learning Algorithms for Big Data Analysis", which serves as a second volume building upon the success of the inaugural edition. As we delve further into the exascale era, the volume and complexity of data generated and collected are reaching unprecedented levels. In this context, machine learning methodologies have emerged as invaluable tools for managing, processing, and extracting meaningful insights from vast datasets.

This Special Issue aims to showcase cutting-edge algorithms and innovative approaches within the realm of machine learning, ranging from traditional support vector machines (SVMs) to sophisticated deep neural networks. We invite researchers to contribute their original work, emphasizing the development of novel algorithms that enhance big data analysis workflows. Whether addressing data reduction, prediction, feature detection, or other tasks critical in large-scale data analytics, we welcome contributions that push the boundaries of machine learning applications.

Join us in advancing the field of big data analysis through the lens of machine learning. We look forward to receiving your high-quality submissions.

Dr. Ayan Biswas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Related Special Issue

Published Papers (1 paper)

Research

27 pages, 13596 KiB

Open AccessArticle

A Piecewise Linear Regression Model Ensemble for Large-Scale Curve Fitting

by Santiago Moreno-Carbonell and Eugenio F. Sánchez-Úbeda

Algorithms 2024, 17(4), 147; https://0-doi-org.brum.beds.ac.uk/10.3390/a17040147 - 30 Mar 2024

Viewed by 658

Abstract

The Linear Hinges Model (LHM) is an efficient approach to flexible and robust one-dimensional curve fitting under stringent high-noise conditions. However, it was initially designed to run in a single-core processor, accessing the whole input dataset. The surge in data volumes, coupled with the increase in parallel hardware architectures and specialised frameworks, has led to a growth in interest and a need for new algorithms able to deal with large-scale datasets and techniques to adapt traditional machine learning algorithms to this new paradigm. This paper presents several ensemble alternatives, based on model selection and combination, that allow for obtaining a continuous piecewise linear regression model from large-scale datasets using the learning algorithm of the LHM. Our empirical tests have proved that model combination outperforms model selection and that these methods can provide better results in terms of bias, variance, and execution time than the original algorithm executed over the entire dataset. Full article

(This article belongs to the Special Issue Machine Learning Algorithms for Big Data Analysis (2nd Edition))

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