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Algorithms
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Machine Learning Algorithms for Big Data Analysis (2nd Edition)
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Machine Learning Algorithms for Big Data Analysis (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 2721

Special Issue Editor

Dr. Ayan Biswas

E-Mail Website
Guest Editor
Los Alamos National Laboratory, Los Alamos, NM 87544, USA
Interests: visualization; data analysis; graphics
Special Issues, Collections and Topics in MDPI journals

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.

Keywords

  • machine learning algorithms
  • streaming
  • parallel
  • computer vision
  • image processing
  • big data analysis

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Related Special Issue

Published Papers (2 papers)

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Research

22 pages, 1086 KiB  
Article
Response-Aided Score-Matching Representative Approaches for Big Data Analysis and Model Selection under Generalized Linear Models
by Duo Zheng, Keren Li and Jie Yang
Algorithms 2024, 17(10), 456; https://doi.org/10.3390/a17100456 - 14 Oct 2024
Viewed by 536
Abstract
In this paper, we propose an efficient method called the response-aided score-matching representative (RASMR) approach to facilitate massive data model selection and data analysis with generalized linear models (GLMs) and a predetermined data partition due to data localization. Similar to the original score-matching [...] Read more.
In this paper, we propose an efficient method called the response-aided score-matching representative (RASMR) approach to facilitate massive data model selection and data analysis with generalized linear models (GLMs) and a predetermined data partition due to data localization. Similar to the original score-matching representative (SMR) approach, RASMR constructs an artificial data point, called the representative, for each data block. It then fits a GLM on the representative dataset, which provides not only an efficient approach for massive data analysis but also an ideal solution in response to privacy concerns by avoiding the transfer of sensitive data. By further splitting the data blocks according to the values of the response variables, RASMR can obtain more accurate parameter estimates than SMR. Furthermore, by theoretical justifications and simulation studies, we show that RASMR can be more efficiently utilized for model selection and variable selection for a massive dataset by approximating the Akaike information criterion (AIC) and the aggregated prediction errors for cross-validation, which are commonly used for choosing the most appropriate statistical model and drawing reliable conclusions. We also apply the proposed RASMR approach to the airline on-time performance data, which consists of 371 data files labeled by month, and show that RASMR can be successfully used for selecting the most appropriate model for real massive data analysis. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Big Data Analysis (2nd Edition))
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27 pages, 13596 KiB  
Article
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://doi.org/10.3390/a17040147 - 30 Mar 2024
Cited by 1 | Viewed by 1568
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 [...] Read more.
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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