Accelerator for crosswise computing reduct

Abstract Attribute reduction, as a technique for selecting qualified attributes which can satisfy the intended constraint related to considered measure, has been widely explored. Notably, one and only one reduct is derived through using one searching strategy in most cases. Nevertheless, only one reduct may be not enough for us to evaluate its effectiveness. To fill such gap, an approach of crosswise computing reduct is proposed for obtaining multiple reducts. The computation of reduct is realized through partitioning the whole data into several groups, and crosswise selecting some groups to form different subsets of data, then computing reducts over these different subsets of data. Moreover, to speed up the process of crosswise computing reduct, an acceleration strategy is designed. The main thinking of our acceleration strategy is to compute the reduct over different subsets of data on the basis of reduct over the whole data. The experimental results over 16 data sets show the following superiorities of our strategy: (1) our approach can decrease the elapsed time of crosswise computing reducts significantly; (2) our approach can not only provide reduct with higher stability, but also maintain the classification performance; (3) the attributes in reduct can provide more stable classification results.