A Proposal of Temporal Case-Base Maintenance Algorithms

Lupiani, Eduardo; Juárez, José M.; Palma, José

doi:10.1007/978-3-319-11209-1_19

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…exploring case-base redefinitions due to the impact of dealing with heterogeneous data like in (Leake & Schack, 2015); velocity, i.e. providing a fast answer to massive data flows like in (Lupiani et al, 2014;Lu et al, 2016); and value i.e. providing best solutions in complex problems like in (Yamamoto et al, 2015).…”

Section: Case-base Maintenancementioning

confidence: 99%

Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes

Torrent-Fontbona

Meléndez

López

2019

Expert Systems with Applications

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People with type 1 diabetes must control their blood glucose level through insulin infusion either with several daily injections or with an insulin pump. However, estimating the required insulin dose is not easy. Recommender systems, mainly based on Case-Based Reasoning (CBR), are being developed to provide recommendations to users. These systems are designed to keep the experiences or cases of the user in a case-base, which requires maintenance to keep system's response accurate and efficient. This paper proposes a case-base maintenance methodology that combines case-base redundancy reduction and attribute weight learning. Contrary to previous approaches designed for classification problems, the maintenance methodology presented in this paper deals with numerical recommendations. It can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. The proposed approach has been tested using the UVA/PADOVA type 1 diabetes simulator and the results demonstrate that it can accomplish better levels of accuracy than other insulin recommender systems mentioned in the literature, when a large number of attributes is considered.

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Section: Case-base Maintenancementioning

confidence: 99%

Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes

Torrent-Fontbona

Meléndez

López

2019

Expert Systems with Applications

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Performance evaluation of fuzzy clustered case-based reasoning

Khan

2020

Journal of Experimental & Theoretical Artificial Intelligen

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Maintenance of Case Bases: Current Algorithms after Fifty Years

Juárez

Craw

Lopez-Delgado

2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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Case-Based Reasoning (CBR) learns new knowledge from data and so can cope with changing environments. CBR is very different from model-based systems since it can learn incrementally as new data is available, storing new cases in its case-base. This means that it can benefit from readily available new data, but also case-base maintenance (CBM) is essential to manage the cases, deleting and compacting the case-base. In the 50th anniversary of CNN (considered the first CBM algorithm), new CBM methods are proposed to deal with the new requirements of Big Data scenarios. In this paper, we present an accessible historic perspective of CBM and we classify and analyse the most recent approaches to deal with these requirements.

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A Proposal of Temporal Case-Base Maintenance Algorithms

Cited by 3 publications

References 20 publications

Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes

Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes

Performance evaluation of fuzzy clustered case-based reasoning

Maintenance of Case Bases: Current Algorithms after Fifty Years

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