Advances in Case-Based Reasoning: 6th European Conference, by Sarah Zelikovitz, Haym Hirsh (auth.), Susan Craw, Alun PDF

By Sarah Zelikovitz, Haym Hirsh (auth.), Susan Craw, Alun Preece (eds.)

ISBN-10: 3540441093

ISBN-13: 9783540441090

The papers accrued during this quantity have been awarded on the sixth ecu C- ference on Case-Based Reasoning (ECCBR 2002) held on the Robert Gordon college in Aberdeen, united kingdom. This convention a chain of very succe- ful well-established biennial eu workshops held in Trento, Italy (2000), Dublin, eire (1998), Lausanne, Switzerland (1996), and Paris, France (1994), after the preliminary workshop in Kaiserslautern, Germany (1993). those conferences have a historical past of attracting ?rst-class ecu and overseas researchers and practitioners within the years interleaving with the biennial foreign co- terpart ICCBR; the 4th ICCBR convention was once held in Vancouver, Canada in 2001. lawsuits of ECCBR and ICCBR meetings are frequently released through Springer-Verlag of their LNAI sequence. Case-Based Reasoning (CBR) is an AI problem-solving method the place pr- lems are solved by way of retrieving and reusing suggestions from related, formerly solved difficulties, and probably revising the retrieved option to re?ect di?erences - tween the hot and retrieved difficulties. Case wisdom shops the formerly solved difficulties and is the most wisdom resource of a CBR method. a prime concentration of CBR learn is the illustration, acquisition and upkeep of case wisdom. lately different wisdom resources were famous as very important: indexing, similarity and edition wisdom. Signi?cant wisdom engine- ing e?ort should be wanted for those, and so the illustration, acquisition and upkeep of CBR wisdom extra usually became important.

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Extra info for Advances in Case-Based Reasoning: 6th European Conference, ECCBR 2002 Aberdeen, Scotland, UK, September 4–7, 2002 Proceedings

Example text

Further we assume ΩF to be the (universal) set of findings (p = v), where p ∈ ΩP is a parameter and v ∈ dom(p) is a possible value. Let CB be the case base containing all available cases that have been solved previously. A case c ∈ CB is defined as a tuple c = (Fc , Dc , Ic ) (1) 30 Joachim Baumeister, Martin Atzm¨uller, and Frank Puppe where Fc ⊆ ΩF is the set of findings observed in the case c. These findings are commonly called problem description. The set Dc ⊆ ΩD is the set of diagnoses describing the solution for this case.

3 Learning Diagnostic Profiles from Cases The available cases usually contain more than one diagnosis (multiple faults). This characteristics makes it difficult to generate exact profiles for each single diagnosis, because it is not obvious, which findings are caused by the single diagnoses. Since we had a sufficient number of cases containing each diagnosis but rare repetitions of combinations of the diagnoses, we applied a statistical method for learning the most frequent covered symptoms of a diagnosis.

Number of solved cases in experiments E0–E4 E2 E3 E4 Fig. 2. Mean accuracy for the 40 most similar cases retrieved in E0–E4. Inductive Learning for Case-Based Diagnosis with Multiple Faults 41 cases in E0–E4, which suggests the trend, that learned knowledge improves the quality of the solved cases. The results presented above are quite promising. Nevertheless, we see enhancements for the number of solved cases and intersection accuracy, when applying a more detailed integration of diagnosis hierarchies into the data preprocessing step and when assessing the intersection accuracy.

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Advances in Case-Based Reasoning: 6th European Conference, ECCBR 2002 Aberdeen, Scotland, UK, September 4–7, 2002 Proceedings by Sarah Zelikovitz, Haym Hirsh (auth.), Susan Craw, Alun Preece (eds.)


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