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Cuesta MCorresponding AuthorLancho C.AuthorFernandez-Isabel A.AuthorCano E.l.AuthorMartín de diego I.Author

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January 8, 2024
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CSViz: Class Separability Visualization for high-dimensional datasets

Publicated to:Applied Intelligence. 54 (1): 924-946 - 2024-01-01 54(1), DOI: 10.1007/s10489-023-05149-4

Authors: Cuesta, Marina; Lancho, Carmen; Fernandez-Isabel, Alberto; Cano, Emilio L; De Diego, Isaac Martin

Affiliations

Data Science Laboratory, Rey Juan Carlos University, c/ Tulipán, s/n, Móstoles, 28933, Spain - Author
Rey Juan Carlos Univ, Data Sci Lab, C Tulipan S-N, Mostoles 28933, Spain - Author

Abstract

Abstract: Data visualization is an essential task during the lifecycle of any Data Science (DS) project, particularly during the Exploratory Data Analysis (EDA) for a correct data preparation and understanding. In classification problems, data visualization is useful for revealing the existence of class separability patterns within the dataset. This information is very valuable and can be later used during the process of building a Machine Learning (ML) model. High-Dimensional Data (HDD) arise as one of the biggest challenges in DS. HDD require special treatment since traditional visualization techniques, such as the scatterplot matrix (SPLOM), have limitations when dealing with them due to space restrictions. Other visualization methods involve dimensionality reduction techniques, which can lead to losing important information and reducing the interpretability of the data. In this paper, the Class Separability Visualization (CSViz) method is introduced as a new Visual Analytics (VA) approach to address the challenge of visualizing labeled HDD through subspaces. The proposed method enables an overview of the class separability offering a series of 2-Dimensional subspaces visualizations containing exclusive subsets of points of the original variables that encompass the most valuable and significant separable patterns. The proposed method is tested over 50 datasets with different characteristics providing promising results. In all cases, more than 90% of the data observations are shown with three plots or less. Hence, the presented CSViz significantly eases the EDA by reducing the number of plots to be inspected in a SPLOM and thus, the amount of time invested in it. Graphical Abstract: CSViz graphical abstract[Figure not available: see fulltext.]. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

analyticsclass separabilityclassificationhigh-dimensional datasub-dimensional spacevisual analyticsClass separabilityData visualizationHigh-dimensional dataSub-dimensional spaceVisual analyticsVisual exploration

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Applied Intelligence due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 84/204, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Computer Science, Artificial Intelligence. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Artificial Intelligence.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-08-07:

  • WoS: 1
  • Scopus: 1

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-08-07:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 4.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 8 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 8.45.
  • The number of mentions on the social network X (formerly Twitter): 10 (Altmetric).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Cuesta Santa Teresa, Marina) and Last Author (Martín de Diego, Isaac).

the author responsible for correspondence tasks has been Cuesta Santa Teresa, Marina.