Fuzzy similarity-based rough set method for case-based reasoning and its application in tool selection

2006 ◽  
Vol 46 (2) ◽  
pp. 107-113 ◽  
Author(s):  
Ya-jun Jiang ◽  
Jun Chen ◽  
Xue-yu Ruan
Keyword(s):  
Rough Set ◽  
Case Based Reasoning ◽  
Tool Selection ◽  
Fuzzy Similarity ◽  
Case Based

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.

A developed case-based reasoning system for machine tool selection

2017 ◽  
Vol 24 (5) ◽  
pp. 1364-1385 ◽  
Author(s):  
Shankar Chakraborty ◽  
Soumava Boral
Keyword(s):  
Machine Tool ◽  
Expert Knowledge ◽  
Fine Tuning ◽  
Selection Problem ◽  
Case Based Reasoning ◽  
Tool Selection ◽  
Diverse Range ◽  
Content Type ◽  
Machine Tool Selection ◽  
Case Based

Purpose Subtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several types of available machine tools are utilized to carry out this manufacturing operation. Selection of the most appropriate machine tool is thus one of the most crucial factors in deciding the success of a manufacturing organization. Ill-suited machine tool may often lead to reduced productivity, flexibility, precision and poor responsiveness. Choosing the best suited machine tool for a specific machining operation becomes more complex, as the process engineers have to consider a diverse range of available alternatives based on a set of conflicting criteria. The paper aims to discuss these issues. Design/methodology/approach Case-based reasoning (CBR), an amalgamated domain of artificial intelligence and human cognitive process, has already been proven to be an effective tool for ill-defined and unstructured problems. It imitates human reasoning process, using specific knowledge accumulated from the previously encountered situations to solve new problems. This paper elucidates development and application of a CBR system for machine tool selection while fulfilling varying user defined requirements. Here, based on some specified process characteristic values, past similar cases are retrieved and reused to solve a current machine tool selection problem. Findings A software prototype is also developed in Visual BASIC 6.0 and three real time examples are illustrated to validate the application potentiality of CBR system for the said purpose. Originality/value The developed CBR system for machine tool selection retrieves a set of similar cases and selects the best matched case nearest to the given query set. It can successfully provide a reasonable solution to a given machine tool selection problem where there is a paucity of expert knowledge. It can also guide the process engineers in setting various parametric combinations for achieving maximum machining performance from the selected machine tool, although fine-tuning of those settings may often be required.

scholarly journals Multi-Attribute Decision Making Based on Attribute Importance Degree and Case-Based Reasoning

2013 ◽  
Vol 13 (Special-Issue) ◽  
pp. 62-74 ◽  
Author(s):  
Zhong Wu ◽  
Ruixia Yan
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Rough Set Theory ◽  
Importance Measure ◽  
Case Based Reasoning ◽  
Relative Importance ◽  
Attribute Importance ◽  
Decision Making Problem ◽  
Multi Attribute Decision Making ◽  
Case Based

Abstract To tackle a multi-attribute decision making problem, rough set and casebased reasoning are often combined. However, the reduction in a rough set is always complex. In this paper we provide a new relative importance measure about the unitary attributes values by ranking the relative importance of the attributes in the rough set theory. A new rough set model based on ranking the relative importance of the attributes is built and its properties are studied. Then unitary attributes values are utilized to compute the similarity of rules in case-based reasoning, for there might be incompletely match or miss values. A new multiattribute decision making based on case-based reasoning and a rough set based on the ranking relative importance of the attributes is constructed, which obtains rules, avoiding reduction and rule extraction.

Rough set approach to case-based reasoning application

2004 ◽  
Vol 26 (3) ◽  
pp. 369-385 ◽  
Author(s):  
Chun-Che Huang ◽  
Tzu-Liang (Bill) Tseng
Keyword(s):  
Rough Set ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals A combined data mining approach using rough set theory and case-based reasoning in medical datasets

2014 ◽  
Vol 3 (3) ◽  
pp. 285-294 ◽  
Author(s):  
Mohammad Taghi Rezvan ◽  
Ali Zeinal Hamadani ◽  
Babak Saffari ◽  
Ali Shalbafzadeh
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Case Based Reasoning ◽  
Data Mining Approach ◽  
Case Based ◽  
Combined Data

A hybrid approach of rough set and case-based reasoning to remanufacturing process planning

2016 ◽  
Vol 30 (1) ◽  
pp. 19-32 ◽  
Author(s):  
Zhigang Jiang ◽  
Ya Jiang ◽  
Yan Wang ◽  
Hua Zhang ◽  
Huajun Cao ◽  
...  
Keyword(s):  
Process Planning ◽  
Rough Set ◽  
Hybrid Approach ◽  
Case Based Reasoning ◽  
Case Based

ATTRIBUTE SELECTION USING ROUGH SETS IN SOFTWARE QUALITY CLASSIFICATION

2009 ◽  
Vol 16 (01) ◽  
pp. 73-89 ◽  
Author(s):  
TAGHI M. KHOSHGOFTAAR ◽  
LOFTON A. BULLARD ◽  
KEHAN GAO
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Software Metrics ◽  
Rough Set Theory ◽  
Training Data ◽  
Case Based Reasoning ◽  
Classification Models ◽  
Data Set ◽  
Irrelevant Attributes ◽  
Case Based

Finding techniques to reduce software developmental effort and produce highly reliable software is an extremely vital goal for software developers. One method that has proven quite useful is the application of software metrics-based classification models. Classification models can be constructed to identify faulty components in a software system with high accuracy. Significant research has been dedicated towards developing methods for improving the quality of software metrics-based classification models. It has been shown in several studies that the accuracy of these models improves when irrelevant attributes are identified and eliminated from the training data set. This study presents a rough set theory approach, based on classical set theory, for identifying and eliminating irrelevant attributes from a training data set. Rough set theory is used to find small groups of attributes, determined by the relationships that exist between the objects in a data set, with comparable discernibility as larger sets of attributes. This allows for the development of simpler classification models that are easy for analyst to understand and explain to others. We built case-based reasoning models in order to evaluate their classification performance on the smaller subsets of attributes selected using rough set theory. The empirical studies demonstrated that by applying a rough set approach to find small subsets of attributes we can build case-based reasoning models with an accuracy comparable to, and in some cases better than, a case-based reasoning model built with a complete set of attributes.

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