Apply fuzzy case-based reasoning to knowledge acquisition of product style

2009 ◽  
Author(s):  
He Xiaodong ◽  
Wu Jianwu ◽  
Shi Fuqian ◽  
Cai Haiyan
Keyword(s):  
Knowledge Acquisition ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals Metode Modified Weighted Minkowski Untuk Pengembangan Sistem Penalaran Berbasis Kasus

2017 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Edi Faizal
Keyword(s):  
Data Collection ◽  
Knowledge Acquisition ◽  
Error Rate ◽  
Case Based Reasoning ◽  
Test Results ◽  
Research Focus ◽  
Accuracy Rate ◽  
Different Levels ◽  
Case Based ◽  
Representation Of Knowledge

Knowledge acquisition process is not easy, because of the different levels of expertise even though all true. Computer experts had tried other methods to resolve the problem of the acquisition, which is known as case-based reasoning. Representation of knowledge in CBR is a collection of previous case. This research focus is the application of CBR for diagnosing womb diseases. The level of similarity is calculated by using the modified weighted Minkowski. Methods of data collection are interviews, observation and study of literature. The test results show the system can be recognize the womb disease correctly is 94.44% (sensitivity), specitifity rate of 57.14%, PPV of 85.00% and 80.00% NPV. The system have an accuracy rate of 84.00% with an error rate of 16.00%.

scholarly journals Error repair and knowledge acquisition via case-based reasoning

1997 ◽  
Vol 91 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Takeshi Kohno ◽  
Susumu Hamada ◽  
Dai Araki ◽  
Shoichi Kojima ◽  
Toshikazu Tanaka
Keyword(s):  
Knowledge Acquisition ◽  
Case Based Reasoning ◽  
Error Repair ◽  
Case Based

Knowledge acquisition tool for case-based reasoning s ystems

1995 ◽  
Vol 9 (2) ◽  
pp. 201-212 ◽  
Author(s):  
A.I. Mechitov ◽  
H.M. Moshkovich ◽  
D.L. Olson ◽  
B. Killingsworth
Keyword(s):  
Knowledge Acquisition ◽  
Case Based Reasoning ◽  
Case Based

CASE-BASED REASONING FOR KNOWLEDGE ACQUISITION SUGGESTIONS

1994 ◽  
Vol 03 (01) ◽  
pp. 23-45
Author(s):  
LEE BECKER ◽  
TODD GUAY
Keyword(s):  
Decision Tree ◽  
Knowledge Acquisition ◽  
Decision Trees ◽  
Current Knowledge ◽  
General Mechanism ◽  
Prototype System ◽  
Case Based Reasoning ◽  
Acquisition Session ◽  
Specific Kind ◽  
Case Based

Case-based suggestion (CBS) is a general mechanism for system-driven interactive knowledge acquisition. CBS applies case-based reasoning to the task of knowledge acquisition. It utilizes previously acquired knowledge embodied in cases to assist the expert during the current knowledge acquisition session. In this work we describe the general CBS technique and illustrate its use during the acquisition of a specific kind of knowledge. A system utilizing CBS was implemented in the acquisition module of a prototype system called ODS, which structures acquired diagnostic knowledge in decision trees. The algorithm used for case-based suggestion by the ODS system and a description of how the decision tree knowledge was represented in the case base is presented. Several evaluation metrics are introduced, and the application of these measures to several experiences of acquiring knowledge with ODS is discussed.

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