2026-04-13T07:05:49Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1940102025-12-05T09:54:29Zcom_2072_1057col_2072_478917col_2072_478920

00925njm 22002777a 4500 dc Nakhjiri, Nariman author Salamó Llorente, Maria author Sànchez i Marrè, Miquel, 1964- author 2023-02-23T09:48:02Z 2023-02-23T09:48:02Z 2020-04-06 2023-02-23T09:48:02Z 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 Reputation for 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 the RBM_ACBR algorithm, which achieves the highest accuracy among the methods in the comparison to a statistically significant degree, and the algorithm, which increases the baseline accuracy while removing, on average, over half of the case base. Intel·ligència artificial Aprenentatge automàtic Artificial intelligence Machine learning Reputation-Based Maintenance in Case-Based Reasoning