论文信息 - Dealing with Imperfect Data by RS-ILP

Dealing with Imperfect Data by RS-ILP

Rough Set theory and Granular Computing (GrC) have a great impact on the study of intelligent information systems. This paper investigates the feasibility of applying Rough Set theory and Granular Computing (GrC) to deal with imperfect data in Inductive Logic Programming (ILP). We propose a hybrid approach, RS-ILP, to deal with some kinds of imperfect data which occur in real-world applications.

Ning Zhong | Chunnian Liu | N. Zhong | Chunnian Liu

[1] Stephen Muggleton,et al. Learning Programs in the Event Calculus , 1997, ILP.

[2] Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[3] Nada Lavrač. Handling Imperfect Data in Inductive Logic Programming , 1996 .

[4] Saso Dzeroski,et al. Inductive Logic Programming and Knowledge Discovery in Databases , 1996, Advances in Knowledge Discovery and Data Mining.

[5] Nada Lavrač,et al. An Introduction to Inductive Logic Programming , 2001 .

[6] N. Zhong,et al. Data Mining: A Probabilistic Rough Set Approach , 1998 .

[7] Lotfi A. Zadeh,et al. Fuzzy sets and information granularity , 1996 .

[8] Lotfi A. Zadeh,et al. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[9] Yiyu Yao,et al. An Analysis of Quantitative Measures Associated with Rules , 1999, PAKDD.

[10] Jerzy W. Grzymala-Busse,et al. Rough Sets , 1995, Commun. ACM.

[11] Yiyu Yao. Granular Computing using Neighborhood Systems , 1999 .

[12] Z. Pawlak. Granularity of knowledge, indiscernibility and rough sets , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[13] Ning Zhong,et al. Rough Problem Settings for Inductive Logic Programming , 1999, RSFDGrC.