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The Variable Precision Rough Set Inductive Logic Programming Model and Strings
Author(s) -
Maheswari V. Uma,
Siromoney Arul,
Mehata K. M.,
Inoue K.
Publication year - 2001
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 - 10.1111/0824-7935.00158
Subject(s) - inductive logic programming , rough set , set (abstract data type) , logic program , variable (mathematics) , algorithm , computer science , logic programming , mathematics , artificial intelligence , programming language , mathematical analysis
The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to Inductive Logic Programming (ILP). The generic Rough Set Inductive Logic Programming (gRS‐ILP) model provides a framework for ILP when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. The gRS‐ILP model is extended in this paper to the VPRSILP model by including features of the VPRS model. The VPRSILP model is applied to strings and an illustrative experiment on transmembrane domains in amino acid sequences is presented.