Attribute reduction with fuzzy rough set is an effective technique for selecting most informative attributes from a given real-valued dataset. However, existing algorithms for attribute reduction with fuzzy rough set have to re-compute a reduct from dynamic data with sample arriving where one sample or multiple samples arrive successively. This is clearly uneconomical from a computational point of view. In order to efficiently find a reduct from such datasets, this paper studies incremental attribute reduction with fuzzy rough sets. At the arrival of one sample or multiple samples, the relative discernibility relation is updated for each attribute. On the basis of the updated relation, an insight into the incremental process of attribute reduction with fuzzy rough sets is gained to reveal how to add new attributes into the current reduct and delete existing attributes from the current reduct. Applying the incremental process, two incremental algorithms for attribute reduction with fuzzy rough sets are presented for one incoming sample and multiple incoming samples, respectively. Experimental comparisons with several non-incremental algorithms and the proposed incremental algorithm for one incoming sample show that our proposed incremental algorithm for multiple incoming samples can efficiently find one reduct with a comparable classification accuracy.
- Fuzzy rough set
- Attribute reduction
- Incremental learning
- Dynamic data
- Relative discernibility relation