scholarly journals Multivariable case adaptation method of case-based reasoning based on multi-case clusters and Multi-output support vector machine for equipment maintenance cost prediction

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mingchi Lin ◽  
Dubo He ◽  
Shengxiang Sun
Keyword(s):  
Support Vector Machine ◽  
Maintenance Cost ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Equipment Maintenance ◽  
Cost Prediction ◽  
Adaptation Method ◽  
Case Based

The Optimization Algorithm of Aviation Equipment Maintenance Cost Forecast and its Applied Research

2013 ◽  
Vol 760-762 ◽  
pp. 1851-1855 ◽  
Author(s):  
Li Zhi Xiao ◽  
Dong Ping Yang ◽  
De Xiang Sun ◽  
Xiao Kun Wang ◽  
Zhi Liang Li
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Optimization Algorithm ◽  
Applied Research ◽  
Forecast Model ◽  
Maintenance Cost ◽  
Support Vector ◽  
Equipment Maintenance ◽  
Forecast Efficiency

The maintenance cost forecast of aviation equipment is a multifactor influenced, non-linear and little samples problem. Aiming at the problem, genetic algorithm (GA) and support vector machine (SVM) were combined to build a GA-SVM forecast model for maintenance cost of aviation equipment. The model used GA to optimize the parameters of SVM, which can avoid the blindness choice of parameters and improve its forecast efficiency. Through the example analysis, the model has more accurate results and extensibility than PSO-SVM, SVM and multivariate linear regression in the forecast of maintenance cost of aviation equipment.

DATABASE CLASSIFICATION BY INTEGRATING A CASE-BASED REASONING AND SUPPORT VECTOR MACHINE FOR INDUCTION

2010 ◽  
Vol 19 (01) ◽  
pp. 31-44 ◽  
Author(s):  
YEN-WEN WANG ◽  
PEI-CHANN CHANG ◽  
CHIN-YUAN FAN ◽  
CHIUNG-HUA HUANG
Keyword(s):  
Support Vector Machine ◽  
Performance Measure ◽  
Current Data ◽  
Classification Model ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Hit Rate ◽  
Proposed Model ◽  
Decision Making System ◽  
Case Based

Database classification suffers from two common problems, i.e., the high dimensionality and nonstationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case-based reasoning technique, a Support Vector Machine (SVM), and Genetic Algorithms to construct a decision-making system for data classification in various database applications. The model is mainly based on the concept that the historic database can be transformed into a smaller case-base together with a group of SVM models. As a result, the model can more accurately respond to the current data under classifying from the inductions by these SVM models generated from these smaller case bases. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different database classification applications. The average hit rate of our proposed model is the highest among others.

R4 Model for Case-Based Reasoning and Its Application for Software Fault Prediction

2016 ◽  
Vol 8 (3) ◽  
pp. 19-38 ◽  
Author(s):  
Ekbal Rashid
Keyword(s):  
Software Quality ◽  
Similarity Measures ◽  
Fault Prediction ◽  
Maintenance Cost ◽  
Quality Level ◽  
Case Based Reasoning ◽  
Quality Prediction ◽  
Error Level ◽  
Software Fault Prediction ◽  
Case Based

Making R4 model effective and efficient I have introduced some new features, i.e., renovation of knowledgebase (KBS) and reducing the maintenance cost by removing the duplicate record from the KBS. Renovation of knowledgebase is the process of removing duplicate record stored in knowledgebase and adding world new problems along with world new solutions. This paper explores case-based reasoning and its applications for software quality improvement through early prediction of error patterns. It summarizes a variety of techniques for software quality prediction in the domain of software engineering. The system predicts the error level with respect to LOC and with respect to development time, and both affects the quality level. This paper also reviews four existing models of case-based reasoning (CBR). The paper presents a work in which I have expanded our previous work (Rashid et al., 2012). I have used different similarity measures to find the best method that increases reliability. The present work is also credited through introduction of some new terms like coefficient of efficiency, i.e., developer's ability.

Design and implementation of construction cost prediction model based on SVM and LSSVM in industries 4.0

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Miao Fan ◽  
Ashutosh Sharma
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Original Data ◽  
Construction Cost ◽  
Support Vector ◽  
Competitive Growth ◽  
Cost Prediction ◽  
Content Type ◽  
Model Based ◽  
Standard Support Vector Machine

PurposeIn order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard Support Vector Machine) and LSSVM (Least Squares Support Vector Machine) is put forward.Design/methodology/approachIn the competitive growth and industries 4.0, the prediction in the cost plays a key role.FindingsAt the same time, the original data is dimensionality reduced. The processed data are imported into the SVM and LSSVM models for training and prediction respectively, and the prediction results are compared and analyzed and a more reasonable prediction model is selected.Originality/valueThe prediction result is further optimized by parameter optimization. The relative error of the prediction model is within 7%, and the prediction accuracy is high and the result is stable.

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