Feature selection for dynamic interval-valued ordered data based on fuzzy dominance neighborhood rough set.

Incremental learning strategy based feature selection approaches can improve the efficiency of reduction algorithm used for datasets with dynamic characteristic, which has attracted increasing research attention. Nevertheless, there is currently no work on incremental feature selection approaches for dynamic interval-valued ordered data. Interval-valued ordered data is a generalized form of single-valued ordered data, which is more widely used in practice. However, the endpoints of the interval numbers are easily polluted by noise, thereby the knowledge granules are very sensitive. Motivated by these two issues, we study incremental feature selection approaches based on a fuzzy dominance neighborhood rough set (FDNRS) for dynamic interval-valued ordered data in this work. First, we propose the FDNRS model for an interval-valued ordered decision system (IvODS) and investigate its related properties. Second, a conditional entropy with robustness is proposed based on the proposed model. This conditional entropy can measure the degree of monotonic consistency of the IvODS, so it is used as a metric and combined with a heuristic feature selection algorithm. Finally, two incremental feature selection algorithms are proposed on the basis of the above researches. Experiments are performed on nine public datasets to evaluate the robustness of the proposed metric and the performance of the incremental algorithms. Experimental results verify that the proposed metric is robust and our incremental algorithms are effective and efficient for updating reducts in dynamic IvODS. [ABSTRACT FROM AUTHOR]