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Analysis of institutional authors

Ruiz, RAuthor

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October 30, 2024
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Feature Selection With Maximal Relevance and Minimal Supervised Redundancy

Publicated to:Ieee Transactions On Cybernetics. 53 (2): 707-717 - 2023-02-01 53(2), DOI: 10.1109/TCYB.2021.3139898

Authors: Wang, YD; Li, XP; Ruiz, R

Affiliations

Henan Univ, Sch Comp & Informat Engn, Inst Data & Knowledge Engn - Author
Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat - Author
Univ Politecn Valencia, Grp Sistemas Optimizac Aplicada, Inst Tecnol Informat Ciudad Politecn Innovac - Author

Abstract

Feature selection (FS) for classification is crucial for large-scale images and bio-microarray data using machine learning. It is challenging to select informative features from high-dimensional data which generally contains many irrelevant and redundant features. These features often impede classifier performance and misdirect classification tasks. In this article, we present an efficient FS algorithm to improve classification accuracy by taking into account both the relevance of the features and the pairwise features correlation in regard to class labels. Based on conditional mutual information and entropy, a new supervised similarity measure is proposed. The supervised similarity measure is connected with feature redundancy minimization evaluation and then combined with feature relevance maximization evaluation. A new criterion max-relevance and min-supervised-redundancy (MRMSR) is introduced and theoretically proved for FS. The proposed MRMSR-based method is compared to seven existing FS approaches on several frequently studied public benchmark datasets. Experimental results demonstrate that the proposal is more effective at selecting informative features and results in better competitive classification performance.

Keywords

ArticleClassificationClassification (of information)Clustering algorithmsConditional mutual informationCorrelationDependencyElectronic mailEntropyFeature extractionFeature selectionFeature selection (fs)Feature selection algorithmFeatures extractionFeatures selectionLearning systemsMachineMicroarray analysisMinimisationMinimizationMutual informationMutual informationsRedundancySimilarity measureSupervised similarity measure

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Ieee Transactions On Cybernetics 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, 2023, it was in position 4/84, thus managing to position itself as a Q1 (Primer Cuartil), in the category Automation & Control Systems. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 5.52. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 4.44 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 33.46 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-17, the following number of citations:

  • WoS: 34
  • Scopus: 36

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 2025-07-17:

  • 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: 13.
  • 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: 20 (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: 0.25.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: China.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (Ruiz Carrasco, Reyes Alejandro).