Hybrid similarity measure for retrieval in case-based reasoning systems and its applications for computer numerical control turret design

2016 ◽  
Vol 232 (5) ◽  
pp. 918-927 ◽  
Author(s):  
Haiqiao Wang ◽  
Beibei Sun ◽  
Xianfa Shen
Keyword(s):  
Similarity Measure ◽  
Computer Numerical Control ◽  
Structure Design ◽  
Numerical Control ◽  
Design Knowledge ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Reasoning Systems ◽  
Global Similarity ◽  
Case Based

Analyzing the history of design is helpful in creating the computer numerical control turret design. Knowledge acquired from reasonably organizing and reusing designs may contribute to establishing computer numerical control turret design tasks. This article presents the results of our study on the representation and reuse of computer numerical control turret design knowledge and highlights the application of a case-based reasoning method in the structure design of a computer numerical control turret. The primary step in case-based reasoning systems is case retrieval where the similarity measure plays a significant role. The objective of this study is to develop a new method for a hybrid similarity measure with five formats of attribute values: crisp symbols, crisp numbers, fuzzy numbers, fuzzy linguistic variables, and fuzzy intervals. First, a hybrid similarity measure for a mixture of crisp and fuzzy sets is proposed for retrieving cases. Subsequently, a synthesis weight is formed through the combination of the subjective weight and objective weight. The calculation formula of the global similarity, which can retrieve the proper historical case, can be established by combining the hybrid similarity measure and the synthesis weight measure accordingly. Finally, the hybrid similarity measure and weight assignment method were applied in a computer numerical control turret conceptual design case-based reasoning system. The results showed that the global similarity of these five attribute types and the distribution of weight coefficients could improve the accuracy of case retrieval, which would help designers achieve the goal of rapid design.

scholarly journals A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4605 ◽  
Author(s):  
Zhai ◽  
Ortega ◽  
Castillejo ◽  
Beltran
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Similarity Measure ◽  
Support System ◽  
Similarity Measures ◽  
Experimental Result ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
And Performance ◽  
Case Based

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.

scholarly journals Methods for a similarity measure for clinical attributes based on survival data analysis

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Christian Karmen ◽  
Matthias Gietzelt ◽  
Petra Knaup-Gregori ◽  
Matthias Ganzinger
Keyword(s):  
Overall Survival ◽  
Clinical Trials ◽  
Similarity Measure ◽  
Survival Data ◽  
Similarity Measures ◽  
Case Based Reasoning ◽  
Local Similarity ◽  
Survival Functions ◽  
Global Similarity ◽  
Case Based

Abstract Background Case-based reasoning is a proven method that relies on learned cases from the past for decision support of a new case. The accuracy of such a system depends on the applied similarity measure, which quantifies the similarity between two cases. This work proposes a collection of methods for similarity measures especially for comparison of clinical cases based on survival data, as they are available for example from clinical trials. Methods Our approach is intended to be used in scenarios, where it is of interest to use longitudinal data, such as survival data, for a case-based reasoning approach. This might be especially important, where uncertainty about the ideal therapy decision exists. The collection of methods consists of definitions of the local similarity of nominal as well as numeric attributes, a calculation of attribute weights, a feature selection method and finally a global similarity measure. All of them use survival time (consisting of survival status and overall survival) as a reference of similarity. As a baseline, we calculate a survival function for each value of any given clinical attribute. Results We define the similarity between values of the same attribute by putting the estimated survival functions in relation to each other. Finally, we quantify the similarity by determining the area between corresponding curves of survival functions. The proposed global similarity measure is designed especially for cases from randomized clinical trials or other collections of clinical data with survival information. Overall survival can be considered as an eligible and alternative solution for similarity calculations. It is especially useful, when similarity measures that depend on the classic solution-describing attribute “applied therapy” are not applicable. This is often the case for data from clinical trials containing randomized arms. Conclusions In silico evaluation scenarios showed that the mean accuracy of biomarker detection in k = 10 most similar cases is higher (0.909–0.998) than for competing similarity measures, such as Heterogeneous Euclidian-Overlap Metric (0.657–0.831) and Discretized Value Difference Metric (0.535–0.671). The weight calculation method showed a more than six times (6.59–6.95) higher weight for biomarker attributes over non-biomarker attributes. These results suggest that the similarity measure described here is suitable for applications based on survival data.

scholarly journals A Hybrid Retrieval Strategy for Case-based Reasoning Using Soft Likelihood Functions

2021 ◽  
Author(s):  
Yameng Wang ◽  
Liguo Fei ◽  
Yuqiang Feng ◽  
Yanqing Wang ◽  
Luning Liu
Keyword(s):  
Operator Method ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Local Similarity ◽  
Retrieval Strategy ◽  
Case Retrieval ◽  
Likelihood Functions ◽  
The Core ◽  
Global Similarity ◽  
Case Based

Abstract Case-based reasoning (CBR) is the retrieval of one or more similar cases from an existing case base for the problem to be solved according to the characteristics of the new problem. The core idea of CBR is that similar cases have similar solutions, so whether the CBR system can play a powerful advantage depends on the quality of case retrieval strategy. At present, the commonly used case retrieval algorithm is based on the mean operator method, which is very hard, and a certain local similarity is low will affect the overall result. In order to calculate the global similarity of cases from a new and softer point of view, this paper introduces the soft likelihood functions into case retrieval, combines the soft likelihood functions with KNN, and proposes a hybrid retrieval strategy. The core of the retrieval strategy is to define the global similarity through SLFs, aggregate the local similarity and characteristic similarity together, and also take the attitude characteristics of decision makers into consideration. Through simulation experiments on real data sets, the accuracy rate is more than 81%, which verifies the effectiveness of the retrieval strategy.

scholarly journals An Efficient Case Retrieval Algorithm for Agricultural Case-Based Reasoning Systems, with Consideration of Case Base Maintenance

Agriculture ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 387
Author(s):  
Zhaoyu Zhai ◽  
José-Fernán Martínez Ortega ◽  
Néstor Lucas Martínez ◽  
Huanliang Xu
Keyword(s):  
Poor Performance ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Reasoning Systems ◽  
Case Base ◽  
Low Efficiency ◽  
Association Table ◽  
Smart Agriculture ◽  
Case Based

Case-based reasoning has considerable potential to model decision support systems for smart agriculture, assisting farmers in managing farming operations. However, with the explosive amount of sensing data, these systems may achieve poor performance in knowledge management like case retrieval and case base maintenance. Typical approaches of case retrieval have to traverse all past cases for matching similar ones, leading to low efficiency. Thus, a new case retrieval algorithm for agricultural case-based reasoning systems is proposed in this paper. At the initial stage, an association table is constructed, containing the relationships between all past cases. Afterwards, attributes of a new case are compared with an entry case. According to the similarity measurement, associated similar or dissimilar cases are then compared preferentially, instead of traversing the whole case base. The association of the new case is generated through case retrieval and added in the association table at the step of case retention. The association table is also updated when a closer relationship is detected. The experiment result demonstrates that our proposal enables rapid case retrieval with promising accuracy by comparing a fewer number of past cases. Thus, the retrieval efficiency of our proposal outperforms typical approaches.

scholarly journals Development of Ground Special Vehicle PHM with Case-Based Reason Model

2021 ◽  
Vol 11 (10) ◽  
pp. 4494
Author(s):  
Qicai Wu ◽  
Haiwen Yuan ◽  
Haibin Yuan
Keyword(s):  
Cross Validation ◽  
Optimization Methods ◽  
Engineering Practice ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Data Set ◽  
Attribute Weights ◽  
Special Vehicle ◽  
Weight Allocation ◽  
Case Based

The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications.

scholarly journals Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiwang Zhong ◽  
Tianhua Xu ◽  
Feng Wang ◽  
Tao Tang
Keyword(s):  
Cloud Computing ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Best Practice ◽  
Discrete Event ◽  
Scale Up ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Fault Features ◽  
Case Based

In Discrete Event System, such as railway onboard system, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis process. Efficient text mining of such maintenance data plays an important role in discovering the best-practice repair knowledge from millions of repair verbatims, which help to conduct accurate fault diagnosis and predication. This paper presents a text case-based reasoning framework by cloud computing, which uses the diagnosis ontology for annotating fault features recorded in the repair verbatim. The extracted fault features are further reduced by rough set theory. Finally, the case retrieval is employed to search the best-practice repair actions for fixing faulty parts. By cloud computing, rough set-based attribute reduction and case retrieval are able to scale up the Big Data records and improve the efficiency of fault diagnosis and predication. The effectiveness of the proposed method is validated through a fault diagnosis of train onboard equipment.

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