Cristin-resultat-ID: 2038819
Sist endret: 21. oktober 2022, 08:41
NVI-rapporteringsår: 2022
Resultat
Vitenskapelig artikkel
2022

F-CBR: An Architecture for Federated Case-Based Reasoning

Bidragsytere:
  • Amar Jaiswal
  • Kassaye Yitbarek Yigzaw og
  • Pinar Øzturk

Tidsskrift

IEEE Access
ISSN 2169-3536
e-ISSN 2169-3536
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2022
Publisert online: 2022
Volum: 10
Sider: 75458 - 75471
Open Access

Importkilder

Scopus-ID: 2-s2.0-85134271265

Klassifisering

Vitenskapsdisipliner

Kunnskapsbaserte systemer

Emneord

Case-Based Reasoning • Beslutningsstøtte • Knowledge federation

Beskrivelse Beskrivelse

Tittel

F-CBR: An Architecture for Federated Case-Based Reasoning

Sammendrag

Case-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attempts to solve new problems using past experiences known as cases. Experiences collected in a single case base from an institution or geographical region are seldom sufficient to solve diverse problems, especially in rare situations. Additionally, many institutions do not promote peer-to-peer (p2p) communication or encourage data sharing through such networks to retain autonomy. The paper proposes a federated CBR (F-CBR) architecture to address these challenges. F-CBR enables solving new problems based on similar cases from multiple autonomous CBR systems without p2p communication. We also designed an algorithm to minimize (irrelevant or unsolicited) data sharing in an F-CBR system. We extend the F-CBR design to support institutions with organizational or geographical hierarchies. The F-CBR architecture was implemented and evaluated on two public datasets and a private real-world (non-specific musculoskeletal disorder patient) dataset. The findings demonstrate that the retrieval quality of F-CBR systems is comparable to or better than a single CBR system that persists all the cases on a centralized case base. F-CBR systems address data privacy by incorporating the data minimization principle. We foresee F-CBR as a viable real-world design that can aid in federating legacy CBR systems with minimal or no changes. The CBR systems used in this study are shared on GitHub to support reproducibility.

Bidragsytere

Amar Deep Jaiswal

Bidragsyterens navn vises på dette resultatet som Amar Jaiswal
  • Tilknyttet:
    Forfatter
    ved Institutt for datateknologi og informatikk ved Norges teknisk-naturvitenskapelige universitet

Kassaye Yitbarek Yigzaw

  • Tilknyttet:
    Forfatter
    ved Nasjonalt senter for e-helseforskning ved Universitetssykehuset Nord-Norge HF

Pinar Øzturk

  • Tilknyttet:
    Forfatter
    ved Institutt for datateknologi og informatikk ved Norges teknisk-naturvitenskapelige universitet
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