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Reputation-based maintenance in case-based reasoning

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10.1016/j.knosys.2019.105283
 
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hdl:2117/327564

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Nakhjiri, Nariman
Salamó, Maria
Sànchez-Marrè, MiquelMés informacióMés informacióMés informació
Document typeArticle
Defense date2020-04-06
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Case Base Maintenance algorithms update the contents of a case base in order to improve case-based reasoner performance. In this paper, we introduce a new case base maintenance method called Reputation-Based Maintenance (RBM) with the aim of increasing the classification accuracy of a Case-Based Reasoning system while reducing the size of its case base. The proposed RBM algorithm calculates a case property called Reputationfor each member of the case base, the value of which reflects the competence of the related case. Based on this case property, several removal policies and maintenance methods have been designed, each focusing on different aspects of the case base maintenance. The performance of the RBM method was compared with well-known state-of-the-art algorithms. The tests were performed on 30 datasets selected from the UCI repository. The results show that the RBM method in all its variations achieves greater accuracy than a baseline CBR, while some variations significantly outperform the state-of-the-art methods. We particularly highlight theRBM_ACBR algorithm, which achieves the highest accuracy among the methods in the comparison to a statistically significant degree, and the RBMcr algorithm, which increases the baseline accuracy while removing, on average, over half of the case base
CitationNakhjiri, N.; Salamó, M.; Sànchez-Marrè, M. Reputation-based maintenance in case-based reasoning. "Knowledge-based systems", 6 Abril 2020, vol. 193, p. 105283: 1-105283: 11. 
URIhttp://hdl.handle.net/2117/327564
DOI10.1016/j.knosys.2019.105283
ISSN0950-7051
Publisher versionhttps://www.sciencedirect.com/science/article/abs/pii/S0950705119305799
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  • Departament de Ciències de la Computació - Articles de revista [909]
  • KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic - Articles de revista [109]
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