Neighborhood Attribute Reduction: A Multicriterion Strategy Based on Sample Selection

Abstract

In the rough-set field, the objective of attribute reduction is to regulate the variations of measures by reducing redundant data attributes. However, most of the previous concepts of attribute reductions were designed by one and only one measure, which indicates that the obtained reduct may fail to meet the constraints given by other measures. In addition, the widely used heuristic algorithm for computing a reduct requires to scan all samples in data, and then time consumption may be too high to be accepted if the size of the data is too large. To alleviate these problems, a framework of attribute reduction based on multiple criteria with sample selection is proposed in this paper. Firstly, cluster centroids are derived from data, and then samples that are far away from the cluster centroids can be selected. This step completes the process of sample selection for reducing data size. Secondly, multiple criteria-based attribute reduction was designed, and the heuristic algorithm was used over the selected samples for computing reduct in terms of multiple criteria. Finally, the experimental results over 12 UCI datasets show that the reducts obtained by our framework not only satisfy the constraints given by multiple criteria, but also provide better classification performance and less time consumption.

Ensemble‐Based Neighborhood Attribute Reduction: A Multigranularity View

“…Conditional entropy is another widely accepted measure which can characterize the discriminating ability of condition attributes related to decision attribute. Presently, with respect to different requirements, various definitions of the condition entropies have been proposed [6][7][8][42][43][44]. A typical form of the conditional entropy is shown as follows.…”