Information Systems and Technologies

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (25 November 2023) | Viewed by 1654

Special Issue Editor


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Guest Editor
ISEG, Universidade de Lisboa, 1249-078 Lisboa, Portugal
Interests: information systems; software engineering; online service quality; e-Government; e-Health
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Special Issue Information

Dear Colleagues,

This Special Issue will present extended versions of selected papers presented at the 11st World Conference on Information Systems and Technologies (WorldCist'23). It was held in Pisa, Italy, 4–6 April 2023. 

WorldCist'23 is a global forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences, and concerns in the several perspectives of Information Systems and Technologies.

Submitted papers should be related with one or more of the main themes proposed for the Conference: 

(A) Information and Knowledge Management (IKM);
(B) Organizational Models and Information Systems (OMIS);
(C) Software and Systems Modeling (SSM);
(D) Software Systems, Architectures, Applications, and Tools (SSAAT);
(E) Multimedia Systems and Applications (MSA);
(F) Computer Networks, Mobility, and Pervasive Systems (CNMPS);
(G) Intelligent and Decision Support Systems (IDSS);
(H) Big Data Analytics and Applications (BDAA);
(I) Human–computer Interaction (HCI);
(J) Ethics, Computers, and Security (ECS)
(K) Health Informatics (HIS);
(L) Information Technologies in Education (ITE);
(M) Technologies for Biomedical Applications (TBA)
(N) Information Technologies in Radiocommunications (ITR).

The conference is completely open (all one needs to do is register), and you do not have to be an author or a discussant to attend. Submissions will be peer reviewed and evaluated based on originality, relevance to the conference, contributions, and presentation. Authors of invited papers should be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% copy/paste from the proceedings paper.

Prof. Dr. Álvaro Rocha
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. Information 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

  • information systems
  • systems information technology
  • intelligent systems
  • WorldCIST 23
  • WorldCIST

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Published Papers (1 paper)

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Research

14 pages, 521 KiB  
Article
An Instance- and Label-Based Feature Selection Method in Classification Tasks
by Qingcheng Fan, Sicong Liu, Chunjiang Zhao and Shuqin Li
Information 2023, 14(10), 532; https://doi.org/10.3390/info14100532 - 28 Sep 2023
Cited by 1 | Viewed by 1089
Abstract
Feature selection is crucial in classification tasks as it helps to extract relevant information while reducing redundancy. This paper presents a novel method that considers both instance and label correlation. By employing the least squares method, we calculate the linear relationship between each [...] Read more.
Feature selection is crucial in classification tasks as it helps to extract relevant information while reducing redundancy. This paper presents a novel method that considers both instance and label correlation. By employing the least squares method, we calculate the linear relationship between each feature and the target variable, resulting in correlation coefficients. Features with high correlation coefficients are selected. Compared to traditional methods, our approach offers two advantages. Firstly, it effectively selects features highly correlated with the target variable from a large feature set, reducing data dimensionality and improving analysis and modeling efficiency. Secondly, our method considers label correlation between features, enhancing the accuracy of selected features and subsequent model performance. Experimental results on three datasets demonstrate the effectiveness of our method in selecting features with high correlation coefficients, leading to superior model performance. Notably, our approach achieves a minimum accuracy improvement of 3.2% for the advanced classifier, lightGBM, surpassing other feature selection methods. In summary, our proposed method, based on instance and label correlation, presents a suitable solution for classification problems. Full article
(This article belongs to the Special Issue Information Systems and Technologies)
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