Inductive Learning for Case-Based Diagnosis with Multiple Faults

Baumeister, Joachim; Atzmüller, Martin; Puppe, Frank

doi:10.1007/3-540-46119-1_4

Cited by 18 publications

(10 citation statements)

References 12 publications

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“…Cardie [14] added MBR (Model-Based Reasoning) to CBR. Baumeister et al [15] studied the CBR based on inductive machine learning. Ahn and Kim [16] managed to enhance the predicting capability of CBR by GA (Genetic Algorithm).…”

Section: Cbrmentioning

confidence: 99%

“…Set the minimum support degree threshold value minsup=25%, the result of event SPM is shown as Table 3. 4 <CR> 58.6% ESL 5 <MR, BP> 27.4% ESL 6 <MR, TO> 55.3% ESL 7 <MR, CR> 42.4% ESL 8 <BP, TO> 27.4% ESL 9 <BP, CR> 27.4% ESL 10 <TO, CR> 27.4% ESL 11 <MR, (BP, TO)> 27.4% ESL 12 <MR, BP, CR> 27.4% ESL 13 <MR, TO, CR> 27.4% ESL 14 <(BP, TO), CR> 27.4% ESL 15 <MR, (BP, TO), CR> 27.4% Table 4 is the sequence-pattern support matrix SPS of cases and the result of similarity measure.…”

Section: Calculating Instancementioning

confidence: 99%

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A Two- Step CBR Method Based on Sequential Data

Xiao¹

2016

IJDTA

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Section: Cbrmentioning

confidence: 99%

Section: Calculating Instancementioning

confidence: 99%

A Two- Step CBR Method Based on Sequential Data

Xiao¹

2016

IJDTA

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“…After that, a diagnostic profile contains at least all relevant attribute values for the specified diagnosis. For a more detailed discussion we refer to [18].…”

Section: Definition 5 (Diagnostic Profile)mentioning

confidence: 99%

Semi-automatic learning of simple diagnostic scores utilizing complexity measures

Atzmueller

Baumeister

Puppe

2006

Artificial Intelligence in Medicine

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Objective: Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semiautomatic learning methods can be used to support the domain specialists. They are usually not only interested in the accuracy of the learned knowledge: the understandability and interpretability of the learned models is of prime importance as well. Then, often simple models are more favorable than complex ones. Methods and Material: We propose diagnostic scores as a promising approach for the representation of simple diagnostic knowledge, and present a method for inductive learning of diagnostic scores. It can be incrementally refined by including background knowledge. We present complexity measures for determining the complexity of the learned scores. Results: We give an evaluation of the presented approach using a case base from the fielded system SONOCONSULT. We further discuss that the user can easily balance between accuracy and complexity of the learned knowledge applying the presented measures. Conclusions: We argue that semi-automatic learning methods can support the domain specialist efficiently when building (diagnostic) knowledge systems from scratch. The presented complexity measures allow for an intuitive assessment of the learned patterns.

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“…After that we apply a statistical pruning method, such that the profiles contain typical attributes and findings for a given diagnosis. For a more detailled discussion we refer to [11].…”

Section: Learning Diagnostic Scoresmentioning

confidence: 99%

Inductive Learning of Simple Diagnostic Scores

Atzmueller

Baumeister

Puppe

2003

Medical Data Analysis

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Abstract. Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semi-automatic learning methods can be used to support the expert. We propose diagnostic scores as a promising approach and present a method for inductive learning of diagnostic scores. It can be be refined incrementally by applying different types of background knowledge. We give an evaluation of the presented approach with a real-world case base.

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Inductive Learning for Case-Based Diagnosis with Multiple Faults

Cited by 18 publications

References 12 publications

A Two- Step CBR Method Based on Sequential Data

A Two- Step CBR Method Based on Sequential Data

Semi-automatic learning of simple diagnostic scores utilizing complexity measures

Inductive Learning of Simple Diagnostic Scores

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