
Indexed in
License and use

Grant support
The research leading to the presented results has been undertaken within the AFARCLOUD European Project (Aggregate Farming in the Cloud), under Grant Agreement No. 783221-AFarCloud-H2020-ECSEL-2017-2, and supported in part by the ECSEL JU and in part by the Spanish Ministry of Science, Innovation and Universities under Grant PCI2018-092965. This work has been also partially supported by the China Scholarship Council (CSC).
Analysis of institutional authors
Zhai, ZhaoyuCorresponding AuthorMartinez Ortega, Jose-FernanAuthorCastillejo, PedroAuthorBeltran, VictoriaCorresponding AuthorA Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System
Publicated to:Sensors. 19 (21): E4605- - 2019-11-01 19(21), DOI: 10.3390/s19214605
Authors: Zhai, Zhaoyu; Martinez Ortega, Jose-Fernan; Castillejo, Pedro; Beltran, Victoria
Affiliations
Abstract
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.
Keywords
Quality index
Bibliometric impact. Analysis of the contribution and dissemination channel
The work has been published in the journal Sensors due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2019, it was in position 15/64, thus managing to position itself as a Q1 (Primer Cuartil), in the category Instruments & Instrumentation.
From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 1.81, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jul 2025)
Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-14, the following number of citations:
- WoS: 6
- Scopus: 7
- Europe PMC: 2
- Google Scholar: 13
Impact and social visibility
Leadership analysis of institutional authors
There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (ZHAI, ZHAOYU) and Last Author (BELTRAN MARTINEZ, MARIA VICTORIA).
the authors responsible for correspondence tasks have been ZHAI, ZHAOYU and BELTRAN MARTINEZ, MARIA VICTORIA.