Case-based reasoning and model-based knowledge-acquisition

2006 ◽  
pp. 97-114 ◽  
Author(s):  
Dietmar Janetzko ◽  
Gerhard Strube
Keyword(s):  
Knowledge Acquisition ◽  
Case Based Reasoning ◽  
Model Based ◽  
Case Based

Survey on structure-based case retrieval

1997 ◽  
Vol 12 (01) ◽  
pp. 41-58 ◽  
Author(s):  
FRIEDRICH GEBHARDT
Keyword(s):  
Structural Properties ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Model Based ◽  
Main Components ◽  
Case Based

The main components of case-based reasoning are case retrieval and case reuse. While case retrieval mostly uses attribute comparisons, many other possibilities exist. The case similarity concepts described in the literature that are based on more elaborate structural properties are classified here into five groups: restricted geometric relationships; graphs; semantic nets; model-based similarities; hierarchically structured similarities. Some general topics conclude this survey on structure-based case retrieval methods and systems.

scholarly journals Maintenance of Case Bases: Current Algorithms after Fifty Years

2018 ◽  
Author(s):  
Jose M. Juarez ◽  
Susan Craw ◽  
J. Ricardo Lopez-Delgado ◽  
Manuel Campos
Keyword(s):  
Big Data ◽  
50Th Anniversary ◽  
Case Based Reasoning ◽  
Changing Environments ◽  
Model Based ◽  
New Knowledge ◽  
Case Base ◽  
Case Based

Case-Based Reasoning (CBR) learns new knowledge from data and so can cope with changing environments. CBR is very different from model-based systems since it can learn incrementally as new data is available, storing new cases in its case-base. This means that it can benefit from readily available new data, but also case-base maintenance (CBM) is essential to manage the cases, deleting and compacting the case-base. In the 50th anniversary of CNN (considered the first CBM algorithm), new CBM methods are proposed to deal with the new requirements of Big Data scenarios. In this paper, we present an accessible historic perspective of CBM and we classify and analyse the most recent approaches to deal with these requirements.

Case-based reasoning: a categorized bibliography

1994 ◽  
Vol 9 (4) ◽  
pp. 355-381 ◽  
Author(s):  
Farhi Marir ◽  
Ian Watson
Keyword(s):  
Expert Systems ◽  
Case Based Reasoning ◽  
Human Reasoning ◽  
Model Based ◽  
The Subject ◽  
Case Based

Case-Based Reasoning (CBR) is a fresh reasoning paradigm for the design of expert systems in domains that may not be appropriate for other reasoning paradigms such as model-based reasoning. As a result of this, and because of its resemblance to human reasoning, CBR has attracted increasing interest both from those experienced in developing expert systems and from novices. Although CBR is a relatively new discipline, there are an increasing number of papers and books being published on the subject. In this context, this bibliographic categorization is an accompanying paper to a review of CBR by the same authors. The objective of this paper is to help researchers quickly identify relevant references.

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
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