Label dependency modeling in Multi-Label Naïve Bayes through input space expansion.

Pka Chitra, Saravana Balaji Balasubramanian, Omar Khattab, Mhd Omar Al-Kadri

Abstract read

In one paragraph

Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Embedded feature fusion for multi-label criteria selection via local search strategy and particle swarm optimization.Scientific reports · 2025
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Pka ChitraDepartment of Information Technology, Rathinam Group of Institutions, Coimbatore, Tamil Nadu, India.

Saravana Balaji BalasubramanianDepartment of Computing, De Montfort University Kazakhstan, Almaty, Kazakhstan.

Omar KhattabDepartment of Computer Science and Engineering, Kuwait College of Science and Technology, Kuwait.

Mhd Omar Al-KadriCollege of Computing and Information Technology, University of Doha for Science and Technology, Doha, Qatar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the realm of multi-label learning, instances are often characterized by a plurality of labels, diverging from the single-label paradigm prevalent in conventional datasets. Multi-label techniques often employ a similar feature space to build classification models for every label. Nevertheless, labels typically convey distinct semantic information and should possess their own unique attributes. Several approaches have been suggested to identify label-specific characteristics for creating distinct categorization models. Our proposed methodology seeks to encapsulate and systematically represent label correlations within the learning framework. The innovation of improved multi-label Naïve Bayes (

Indexed as

Heterogeneous feature spaceInput space expansionLabel dependencyMixed joint density distributionMulti-label Naïve Bayesian classification

Identifiers

PMID39896363

DOI10.7717/peerj-cs.2093

PMCPMC11784536

What OpenQuestion holds

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.