Subsampling conflicts to construct better fuzzy rules

Many fuzzy rule induction algorithms have been proposed during the past decade or so. Most of these algorithms tend to scale badly with large dimensions of the feature space, because the underlying heuristics tend to constrain suboptimal features. Often, noisy training instances also influence the size of the resulting rule set. In this paper, an algorithm is discussed that extracts a set of so-called mixed fuzzy rules. These rules can be extracted from feature spaces with diverse types of attributes and handle the corresponding different types of constraints in parallel. The underlying heuristic minimizes the loss of coverage for each rule when a conflict occurs. We present the original algorithm, which avoids conflicts for each pattern individually, and demonstrate how a subsampling strategy improves the resulting rule set, both with respect to performance and the interpretability of the resulting rules