A comparative evaluation of rough sets and probabilistic network algorithms on learning pseudo-independent domains

Authors:
Jae-Hyuck Lee
Affiliations:
Department of Computing and Information Science, University of Guelph, Guelph, Ontario, Canada
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Year:
2005

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Abstract

This study provides a comparison between the rough sets and probabilistic network algorithms in application to learning a pseudo-independent (PI) model, a type of probabilistic models hard to learn by common probabilistic learning algorithms based on search heuristics called single-link lookahead. The experimental result from this study shows that the rough sets algorithm outperforms the common probabilistic network method in learning a PI model. This indicates that the rough sets algorithm can apply to learning PI domains.