Mixed fuzzy rule formation

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 and in addition have trouble dealing with different feature types or noisy data. In this paper, an algorithm is proposed 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 extracted rules depend on individual subsets of only few attributes, which is especially useful in high dimensional feature spaces. The algorithm along with results on several classification benchmarks is presented and how this method can be extended to handle outliers or noisy training instances as well is sketched briefly.