Anzeige
Erschienen in:
2009 | OriginalPaper | Buchkapitel
Research on Complete Algorithms for Minimal Attribute Reduction
verfasst von : Jie Zhou, Duoqian Miao, Qinrong Feng, Lijun Sun
Erschienen in: Rough Sets 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
Minimal attribute reduction plays an important role in both theory and practice, but it has been proved that finding a minimal reduct of a given decision table is a NP-hard problem. Some scholars have also pointed out that current heuristic algorithms are incomplete for minimal attribute reduction. Based on the decomposition principles of a discernibility function, a complete algorithm
CAMARDF
for finding a minimal reduct is put forward in this paper. Since it depends on logical reasoning, it can be applied for all decision tables after their discernibility functions constructed reasonably. The efficiency of
CAMARDF
is illustrated by experiments with UCI data sets further.