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Mathematics
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Applications of Mathematics in Neural Networks and Machine Learning
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Applications of Mathematics in Neural Networks and Machine Learning

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 943

Special Issue Editors

Prof. Dr. Hector Florez

E-Mail Website
Guest Editor
ITI Research Group, Universidad Distrital Francisco Jose de Caldas, Bogota 110231, Colombia
Interests: artificial intelligence; model driven engineering; information systems
Special Issues, Collections and Topics in MDPI journals
Dr. Edward Yapp

E-Mail Website
Guest Editor
Manufacturing System Division, Singapore Institute of Manufacturing Technology, Singapore 138634, Singapore
Interests: artificial intelligence; neural network; deep learning

Special Issue Information

Dear Colleagues,

Neural networks and information as well as learning theory are interconnected fields that have revolutionized the development of artificial intelligence and machine learning, leading to advancements in various domains. Neural networks process information, while information theory provides a framework with which to analyze and quantify information content. Learning theory guides the training process, enabling neural networks to learn from data and improve their performance. Neural networks and information as well as learning theory are based on several mathematical models mainly focused on linear algebra, calculus, probability, and statistics.

This Special Issue invites researchers and practitioners to submit their high-quality original research or review articles that discuss how neural networks are used to process as well as interpret information and how learning theory guides the training process, highlighting the current cutting-edge research findings in this field.

Prof. Dr. Hector Florez
Dr. Edward Yapp
Guest Editors

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • neural networks
  • learning theory
  • machine learning
  • information theory

Published Papers (1 paper)

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Research

31 pages, 6204 KiB  
Article
A Formalization of Multilabel Classification in Terms of Lattice Theory and Information Theory: Concerning Datasets
by Francisco J. Valverde-Albacete and Carmen Peláez-Moreno
Mathematics 2024, 12(2), 346; https://doi.org/10.3390/math12020346 - 21 Jan 2024
Viewed by 606
Abstract
Multilabel classification is a recently conceptualized task in machine learning. Contrary to most of the research that has so far focused on classification machinery, we take a data-centric approach and provide an integrative framework that blends qualitative and quantitative descriptions of multilabel data [...] Read more.
Multilabel classification is a recently conceptualized task in machine learning. Contrary to most of the research that has so far focused on classification machinery, we take a data-centric approach and provide an integrative framework that blends qualitative and quantitative descriptions of multilabel data sources. By combining lattice theory, in the form of formal concept analysis, and entropy triangles, obtained from information theory, we explain from first principles the fundamental issues of multilabel datasets such as the dependencies of the labels, their imbalances, or the effects of the presence of hapaxes. This allows us to provide guidelines for resampling and new data collection and their relationship with broad modelling approaches. We have empirically validated our framework using 56 open datasets, challenging previous characterizations that prove that our formalization brings useful insights into the task of multilabel classification. Further work will consider the extension of this formalization to understand the relationship between the data sources, the classification methods, and ways to assess their performance. Full article
(This article belongs to the Special Issue Applications of Mathematics in Neural Networks and Machine Learning)
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