Tolerance rough set-inductive logic programming (RS-ILP)

Authors:
Rifeng Wang;Peihe Tang;Chungui Li;Hao Liu
Affiliations:
Department of Comuter Science, Guangxi University of Technology, Liuzhou, China;Department of Comuter Science, Guangxi University of Technology, Liuzhou, China;Department of Comuter Science, Guangxi University of Technology, Liuzhou, China;Department of Comuter Science, Guangxi University of Technology, Liuzhou, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Year:
2009

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Abstract

Inductive Logic Programming (ILP) is one of the main approaches to relational learning, with the stronger expressive power and the ease of using background knowledge. However, compared with the traditional attribute-value learning methods, it is much less mature for ILP to deal with imperfect data. This paper applies the Tolerance Rough Set to ILP to further extend the RS-ILP model. We first investigate a new kind of Tolerance Rough Set model, which can deal with imperfect data (nominal and numerical) consistently, and then propose a Tolerance RS-ILP model, in which the tolerance rough problem settings are given, which can handle missing data, indiscernible data, and have a certain abilities to deal with noise data and imperfect output.