Adaptive case-based reasoning using support vector regression

2013 ◽  
Author(s):  
M. Sharifi ◽  
M. Naghibzadeh ◽  
M. Rouhani
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Case Based

Combining case-based reasoning systems and support vector regression to evaluate the atmosphere–ocean interaction

2010 ◽  
Vol 30 (1) ◽  
pp. 155-177 ◽  
Author(s):  
Juan F. De Paz ◽  
Javier Bajo ◽  
Angélica González ◽  
Sara Rodríguez ◽  
Juan M. Corchado
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Reasoning Systems ◽  
Case Based

scholarly journals Research on the model for tobacco disease prevention and control based on case-based reasoning and knowledge graph

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1947-1952 ◽  
Author(s):  
Lichuan Gu ◽  
Yingchun Xia ◽  
Xiaohui Yuan ◽  
Chao Wang ◽  
Jun Jiao
Keyword(s):  
Disease Prevention ◽  
Prevention And Control ◽  
Principal Component ◽  
Support Vector ◽  
Knowledge Graph ◽  
Case Based Reasoning ◽  
Long Time ◽  
And Control ◽  
Tobacco Disease ◽  
Case Based

Tobacco is one of the most important economic crops in China. The yield and quality of tobacco reduce severely because of long-time disease invasion. Currently, the main focus of researches on tobacco disease prevention and control is the diagnosis of disease that has occurred, which ignores to predict disease before it outbreaks. Therefore, in this paper, we follow the idea that prediction is used before disease prevention and control and study the model for tobacco disease prevention and control by using knowledge graph and case-based reasoning (CBR). In order to implement the model, we choose tobacco mosaic virus (TMV) as research object and follow the following methods to prevent occurrence of that. At first, a method to predicting environmental factors by using principal component analysis (PCA) and support vector machine (SVM) is proposed. According to the prediction result, knowledge graph and CBR are used to retrieve the most similarity case and finally determine the best solution. Experimental results demonstrate that our model can achieve high accuracy and give the most appropriate scheme for disease prevention and control.

scholarly journals Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7356
Author(s):  
Chenxi Ding ◽  
Aijun Yan
Keyword(s):  
Fault Detection ◽  
Back Propagation ◽  
Waste Incineration ◽  
Training Sample ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Detection Model ◽  
Msw Incineration ◽  
Incineration Process ◽  
Case Based

Fault detection in the waste incineration process depends on high-temperature image observation and the experience of field maintenance personnel, which is inefficient and can easily cause misjudgment of the fault. In this paper, a fault detection method is proposed by combining stochastic configuration networks (SCNs) and case-based reasoning (CBR). First, a learning pseudo metric method based on SCNs (SCN-LPM) is proposed by training SCN learning models using a training sample set and defined pseudo-metric criteria. Then, the SCN-LPM method is used for the case retrieval stage in CBR to construct the fault detection model based on SCN-CBR, and the structure, algorithmic implementation, and algorithmic steps are given. Finally, the performance is tested using historical data of the MSW incineration process, and the proposed method is compared with typical classification methods, such as a Back Propagation (BP) neural network, a support vector machine, and so on. The results show that this method can effectively improve the accuracy of fault detection and reduce the time complexity of the task and maintain a certain application value.

scholarly journals A CASE-BASED REASONING SYSTEM FOR GENOTYPIC PREDICTION OF HIV-1 CO-RECEPTOR TROPISM

2013 ◽  
Vol 11 (04) ◽  
pp. 1350006 ◽  
Author(s):  
MARK C. EVANS ◽  
AGNES C. PAQUET ◽  
WEI HUANG ◽  
LAURA NAPOLITANO ◽  
ARNE FRANTZELL ◽  
...  
Keyword(s):  
Prediction Algorithm ◽  
Clinical Samples ◽  
Support Vector ◽  
Case Based Reasoning ◽  
V3 Loop ◽  
Prediction Tool ◽  
Enhanced Sensitivity ◽  
Scoring Matrices ◽  
Tropism Prediction ◽  
Case Based

Accurate co-receptor tropism (CRT) determination is critical for making treatment decisions in HIV management. We created a genotypic tropism prediction tool by utilizing the case-based reasoning (CBR) technique that attempts to solve new problems through applying the solution from similar past problems. V3 loop sequences from 732 clinical samples with diverse characteristics were used to build a case library. Additional sequence and molecular properties of the V3 loop were examined and used for similarity assessment. A similarity metric was defined based on each attribute's frequency in the CXCR4-using viruses. We implemented three other genotype-based tropism predictors, support vector machines (SVM), position specific scoring matrices (PSSM), and the 11/25 rule, and evaluated their performance as the ability to predict CRT compared to Monogram's enhanced sensitivity Trofile®assay (ESTA). Overall concordance of the CBR based tropism prediction algorithm was 81%, as compared to ESTA. Sensitivity to detect CXCR4 usage was 90% and specificity was at 73%. In comparison, sensitivity of the SVM, PSSM, and the 11/25 rule were 85%, 81%, and 36% respectively while achieving a specificity of 90% by SVM, 75% by PSSM, and 97% by the 11/25 rule. When we evaluated these predictors in an unseen dataset, higher sensitivity was achieved by the CBR algorithm (87%), compared to SVM (82%), PSSM (76%), and the 11/25 rule (33%), while maintaining similar level of specificity. Overall this study suggests that CBR can be utilized as a genotypic tropism prediction tool, and can achieve improved performance in independent datasets compared to model or rule based methods.

scholarly journals Multivariable case-based reason adaptation based on multiple-output support vector regression with similarity-related weight for parametric mechanical design

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880464 ◽  
Author(s):  
Jin Qi ◽  
Jie Hu
Keyword(s):  
Support Vector Regression ◽  
Mechanical Design ◽  
Training Sample ◽  
Support Vector ◽  
Single Output ◽  
Output Structure ◽  
Actual Design ◽  
Comparative Results ◽  
Adaptation Method ◽  
Case Based

Using historical cases’ solutions to obtain feasible solution for new problem is fundamentally to successfully applying case-based reason technique in parametric mechanical design. As a well-known intelligent algorithm, the formulation of support vector regression has been taken for case-based reason adaptation, but the standard support vector regression can only be used as a univariate adaptation method because of its single-output structure, which would result in the ignorance of the possible interrelations among solution outputs. To handle the complicated case adaptation task with large number of problem inputs and solution outputs more efficiently, this study investigates the possibility of multivariable case-based reason adaptation with multiple output by applying multiple-output support vector regression. Furthermore, inspired by the fact that training sample which contains two closer cases can provide more useful information than others, this study adds the similarity-related weight into multiple-output support vector regression and gives high weights to the information provided by such useful training sample during multi-dimensional regression estimation. The superiority of proposed multiple-output support vector regression with similarity-related weight is validated by the actual design example and quantitative comparisons with other adaptation methods. The comparative results indicate that multiple-output support vector regression with similarity-related weight achieves the best performance for large-quantity case-based reason adaptation because of its higher accuracy and relatively lower cost.

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