Fast anytime retrieval with confidence in large-scale temporal case bases

This work is about speeding up retrieval in Case-Based Reasoning (CBR) for large-scale case bases (CBs) comprised of temporally related cases in metric spaces. A typical example is a CB of electronic health records where consecutive sessions of a patient forms a sequence of related cases. k-Nearest...

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Detalles Bibliográficos
Autores: Mulayim, Mehmet Oguz, Arcos Rosell, Josep Lluís
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/234632
Acceso en línea:http://hdl.handle.net/10261/234632
Access Level:acceso abierto
Palabra clave:Large-scale case-based reasoning
Exact and approximate k-nearest neighbor search
Anytime algorithms
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spelling Fast anytime retrieval with confidence in large-scale temporal case basesMulayim, Mehmet OguzArcos Rosell, Josep LluísLarge-scale case-based reasoningExact and approximate k-nearest neighbor searchAnytime algorithmsThis work is about speeding up retrieval in Case-Based Reasoning (CBR) for large-scale case bases (CBs) comprised of temporally related cases in metric spaces. A typical example is a CB of electronic health records where consecutive sessions of a patient forms a sequence of related cases. k-Nearest Neighbors (kNN) search is a widely used algorithm in CBR retrieval. However, brute-force kNN is impossible for large CBs. As a contribution to efforts for speeding up kNN search, we introduce an anytime kNN search methodology and algorithm. Anytime Lazy kNN finds exact kNNs when allowed to run to completion with remarkable gain in execution time by avoiding unnecessary neighbor assessments. For applications where the gain in exact kNN search may not suffice, it can be interrupted earlier and it returns best-so-far kNNs together with a confidence value attached to each neighbor. We describe the algorithm and methodology to construct a probabilistic model that we use both to estimate confidence upon interruption and to automatize the interruption at desired confidence thresholds. We present the results of experiments conducted with publicly available datasets. The results show superior gains compared to brute-force search. We reach to an average gain of 87.18% with 0.98 confidence and to 96.84% with 0.70 confidence.This work has been funded by the project Playing and Singing for the Recovering Brain: Efficacy of Enriched Social-Motivational Musical Interventions in Stroke Rehabilitation (Play&Sing), Spain, 201729.31, Fundació La Marató de TV3, Spain; and, by the project Innobrain, Spain, COMRDI-151-0017 (RIS3CAT comunitats), and Feder, Spain funds.Elsevier BVMinisterio de Ciencia e Innovación (España)Fundació La Marató de TV3Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2021202120202021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/234632reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1016/j.knosys.2020.106374Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2346322026-05-22T06:33:51Z
dc.title.none.fl_str_mv Fast anytime retrieval with confidence in large-scale temporal case bases
title Fast anytime retrieval with confidence in large-scale temporal case bases
spellingShingle Fast anytime retrieval with confidence in large-scale temporal case bases
Mulayim, Mehmet Oguz
Large-scale case-based reasoning
Exact and approximate k-nearest neighbor search
Anytime algorithms
title_short Fast anytime retrieval with confidence in large-scale temporal case bases
title_full Fast anytime retrieval with confidence in large-scale temporal case bases
title_fullStr Fast anytime retrieval with confidence in large-scale temporal case bases
title_full_unstemmed Fast anytime retrieval with confidence in large-scale temporal case bases
title_sort Fast anytime retrieval with confidence in large-scale temporal case bases
dc.creator.none.fl_str_mv Mulayim, Mehmet Oguz
Arcos Rosell, Josep Lluís
author Mulayim, Mehmet Oguz
author_facet Mulayim, Mehmet Oguz
Arcos Rosell, Josep Lluís
author_role author
author2 Arcos Rosell, Josep Lluís
author2_role author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Fundació La Marató de TV3
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Large-scale case-based reasoning
Exact and approximate k-nearest neighbor search
Anytime algorithms
topic Large-scale case-based reasoning
Exact and approximate k-nearest neighbor search
Anytime algorithms
description This work is about speeding up retrieval in Case-Based Reasoning (CBR) for large-scale case bases (CBs) comprised of temporally related cases in metric spaces. A typical example is a CB of electronic health records where consecutive sessions of a patient forms a sequence of related cases. k-Nearest Neighbors (kNN) search is a widely used algorithm in CBR retrieval. However, brute-force kNN is impossible for large CBs. As a contribution to efforts for speeding up kNN search, we introduce an anytime kNN search methodology and algorithm. Anytime Lazy kNN finds exact kNNs when allowed to run to completion with remarkable gain in execution time by avoiding unnecessary neighbor assessments. For applications where the gain in exact kNN search may not suffice, it can be interrupted earlier and it returns best-so-far kNNs together with a confidence value attached to each neighbor. We describe the algorithm and methodology to construct a probabilistic model that we use both to estimate confidence upon interruption and to automatize the interruption at desired confidence thresholds. We present the results of experiments conducted with publicly available datasets. The results show superior gains compared to brute-force search. We reach to an average gain of 87.18% with 0.98 confidence and to 96.84% with 0.70 confidence.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/234632
url http://hdl.handle.net/10261/234632
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1016/j.knosys.2020.106374
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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