Feature selection for clustering based fuzzy modeling

In this paper, we propose a fast feature selection technique for clustering-based fuzzy modeling. The technique involves the creation of 'rough' fuzzy systems quickly from a set of data and chooses the one with the lowest error. The set of features used by the chosen fuzzy system is accepted as the optimal set of features. The effectiveness and efficiency of the proposed technique is validated using artificially generated data.