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QUANTIFICATION OF UNCERTAINTY IN CLASSIFICATION RULES DISCOVERED FROM DATABASES
Author(s) -
Xiang Y.,
Wong S. K. M.,
Cercone N.
Publication year - 1995
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.1995.tb00042.x
Subject(s) - rough set , constraint (computer aided design) , data mining , classification rule , computer science , set (abstract data type) , database , artificial intelligence , machine learning , mathematics , geometry , programming language
We apply rough set constructs to inductive learning from a database. A design guideline is suggested, which provides users the option to choose appropriate attributes, for the construction of data classification rules. Error probabilities for the resultant rule are derived. A classification rule can be further generalized using concept hierarchies. The condition for preventing overgeneralization is derived. Moreover, given a constraint, an algorithm for generating a rule with minimal error probability is proposed.