Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
Likelihood-Based Sampling from Databases for Rule Induction Methods
verfasst von : Shusaku Tsumoto, Shoji Hirano, Hidenao Abe
Erschienen in: Rough Set and Knowledge Technology
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
This paper introduces the idea of log-likelihood ratio to measure the similarity between generated training samples and original tracing samples. The ratio is used as a test statistic to determine whether the statistical information of generated training samples(
S
k
) is almost equivalent to that of original training samples(
S
0
), denoted by
S
0
≃
S
k
. If the test statistic obtained rejects the hypothesis
S
0
≃
S
k
, then these samples are abandoned. Otherwise, the generated samples are accepted and rule induction methods or statistical methods are applied. This method was evaluated to three medical domains. The results show that the proposed method selects training samples which reflect the statistical characteristics of the original training samples although the performance with small samples is not so good.