Similarity, Uncertainty and Case-Based Reasoning in Patdex

Richter, Michael M.; Wess, Stefan

doi:10.1007/978-94-011-3488-0_12

Cited by 38 publications

(10 citation statements)

References 4 publications

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“…Michael Richter and Klaus Althoff in the University of Kaiserslautern applied CBR to complex diagnosis [18]. This has given rise to the PATDEX system [19] and subsequently to the CBR tool S3-Case. In the University of Trondheim, Agnar Aamodt has investigated the learning facet of CBR and the combination of cases and general domain knowledge resulting in CREEK [20][21].…”

Section: Research Backgroundmentioning

confidence: 99%

Theories and Methods for the Emergency Rescue System

Li¹,

Liu²,

Bin³

2012

Novel Approaches and Their Applications in Risk Assessment

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Section: Research Backgroundmentioning

confidence: 99%

Theories and Methods for the Emergency Rescue System

Li¹,

Liu²,

Bin³

2012

Novel Approaches and Their Applications in Risk Assessment

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“…Here we follow the idea developed for the Patdex-1 system [26]. Given p one selects c' = (p',s') such that p' is most similar to p with respect to the case base.…”

Section: Figure 5 Parallel Retrieval With Global Similarity Metricmentioning

confidence: 99%

“…Based on how successful the retrieval is, symptom-to-diagnosis associations are strengthened or weakened over time. Richter and Wess [26] proposes a method based on neural networks for such learning. Our method, based on the Creek architecture [18], incrementally adjusts similarity threshold values, depending on how successful an index has been in retrieving a useful case.…”

Section: Local Dynamic Similarity Metricmentioning

confidence: 99%

A new approach to applicable memory-based reasoning

Bø¹,

Aamodt²

1999

Journal of Experimental & Theoretical Artificial Intelligen

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To appropriately utilize the rapidly growing amount of data and information is a big challenge for people and organizations. Standard information retrieval methods, using sequential processing combined with syntax-based indexing and access methods, have not been able to adequately handle this problem. We are currently investigating a different approach, based on a combination of massive parallel processing with case-based (memory-based) reasoning methods. Given the problems of purely syntax-based retrieval methods, we suggest ways of incorporating general domain knowledge into memory-based reasoning. Our approach is related to the properties of the parallel processing microchip MS160, particularly targeted at fast information retrieval from very large data sets. Within this framework different memory-based methods are studied, differing in the type and representation of cases, and in the way that the retrieval methods are supported by explicit general domain knowledge. Cases can be explicitly stored information retrieval episodes, virtually stored abstractions linked to document records, or merely the document records themselves. General domain knowledge can be a multi-relational semantic network, a set of term dependencies and relevances, or compiled into a global similarity metric. This paper presents the general framework, discusses the core issues involved, and describes three different methods illustrated by examples from the domain of medical diagnosis.

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“…The CBR provides a method simulating the human way of thinking to reuse the most similar solution of previous problem to help solve the current new problem. Most of the CBR application researches designed a distance measurement formula for calculating the similarity between two attributes [13,14] or two queries [15][16][17]. Gu et al [18] concluded these researches as three categories: case-biased, query-biased and equally-biased similarity calculation methods.…”

Section: Introductionmentioning

confidence: 99%