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

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.