Techniques for efficient empirical induction
Author(s) -
Geoffrey I. Webb
Publication year - 1990
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
DOI - 10.1007/3-540-52062-7_82
Subject(s) - computer science , rule induction , predicate (mathematical logic) , decision tree , algorithm , class (philosophy) , decision rule , set (abstract data type) , empirical research , value (mathematics) , artificial intelligence , data mining , machine learning , mathematics , programming language , statistics
This paper describes the LEI algorithm for empirical induction. The LEI algorithm provides efficient empirical induction for discrete attribute value data. It derives a classification procedure in the form of a set of predicate logic classification rules. This contrasts with the only other efficient approach to exhaustive empirical induction, the derivatives of the CLS algorithm, which present their classification procedures in the form of a decision tree. The LEI algorithm will always find the simplest non-disjunctive rule that correctly classifies all examples of a single class where such a rule exists.
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