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A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System
Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864878/ https://www.ncbi.nlm.nih.gov/pubmed/31652715 http://dx.doi.org/10.3390/s19214605 |
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author | Zhai, Zhaoyu Ortega, José-Fernán Martínez Castillejo, Pedro Beltran, Victoria |
author_facet | Zhai, Zhaoyu Ortega, José-Fernán Martínez Castillejo, Pedro Beltran, Victoria |
author_sort | Zhai, Zhaoyu |
collection | PubMed |
description | Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness. |
format | Online Article Text |
id | pubmed-6864878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68648782019-12-06 A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System Zhai, Zhaoyu Ortega, José-Fernán Martínez Castillejo, Pedro Beltran, Victoria Sensors (Basel) Article Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness. MDPI 2019-10-23 /pmc/articles/PMC6864878/ /pubmed/31652715 http://dx.doi.org/10.3390/s19214605 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhai, Zhaoyu Ortega, José-Fernán Martínez Castillejo, Pedro Beltran, Victoria A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System |
title | A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System |
title_full | A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System |
title_fullStr | A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System |
title_full_unstemmed | A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System |
title_short | A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System |
title_sort | triangular similarity measure for case retrieval in cbr and its application to an agricultural decision support system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864878/ https://www.ncbi.nlm.nih.gov/pubmed/31652715 http://dx.doi.org/10.3390/s19214605 |
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