Integrating rules and cases in learning via case explanation and paradigm shift

• Type: Article in International Scientific Journal
• Date: 2000

Authors

Lopes, AD (Author) Other; Jorge, A (Author) FEP

Journal

Title: Advances in Artificial Intelligence

Vol. 100

Pages: 33-42

ISSN: 1870-4069x

Publisher: NPI

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 5 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-001-12J

DOI: 10.1007/3-540-44399-1_5

Abstract (EN): In this article we discuss in detail two techniques for rule and case integration. Case-based learning is used when the rule language is exhausted. Initially, all the examples are used to induce a set of rules with satisfactory quality. The examples that are not covered by these rules are then handled as cases. The case-based approach used also combines rules and cases internally. Instead of only storing the cases as provided, it has a learning phase where, for each case, it constructs and stores a set of explanations with support and confidence above given thresholds. These explanations have different levels of generality and the maximally specific one corresponds to the case itself. The same case may have different explanations representing different perspectives of the case. Therefore, to classify a new case, it looks for relevant stored explanations applicable to the new case. The different possible views of the case given by the explanations correspond to considering different sets of conditions/features to analyze the case. In other words, they lead to different ways to compute similarity between known cases/explanations and the new case to be classified (as opposed to the commonly used fixed metric).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10