Ensemble Feature Selection Using Neighbourhood Rough Set–Based Multicriterion Fusion

Feature selection refers to finding a compact feature subset in which redundant and irrelevant information is reduced. It is generally aimed at simplifying the learning model and improving the learning performance simultaneously. To further enhance the traditional feature selection, ensemble feature selection has been developed vigorously in recent years. In this study, a novel ensemble feature selection algorithm that fuses multiple scores induced by multiple neighbourhood rough set–based feature evaluation criteria is presented. In detail, three different neighbourhood rough set–based feature evaluation criteria are applied, including neighbourhood approximation quality, neighbourhood recognition rate, and neighbourhood discrimination index. These representative measures serve as ensemble bases to induce multiple feature scores. Then, an aggregation method is proposed to fuse these scores for quantifying the feature importance quality. Finally, a searching algorithm based on this neighbourhood score by multicriterion fusion is devised. Experimental results on 16 UCI data sets prove that the proposed method outperforms other feature selection algorithms in classification task.