Presently, attribute reduction, as one of the most important topics in the field of rough set, has been widely explored from different perspectives. To derive the qualified reduct defined in attribute reduction, forward greedy searching is frequently used. However, the previous researches indicate that such searching strategy may be still computationally expensive if the volume of data is large. In view of this, two frameworks are proposed by considering the relationships between attributes, which aim to accelerate the process of searching reducts. Our consideration is actually realized based on the dissimilarity and similarity between attributes, respectively. The main mechanisms are: (1) for the dissimilarity based approach, the combination of attributes with significant difference instead of one and only one attribute will be added into potential reduct in the process of searching reduct; (2) for the similarity based approach, the candidate attributes which are similar to those attributes in potential reduct will be tentatively ignored instead of being evaluated in the process of searching reduct. The experimental results over 16 UCI data sets demonstrate that whether single granularity or multi-granularity attribute reduction is considered, our proposed approaches can not only generate the reducts which may not lead to poorer performances, but also provide superior time efficiency of calculating reducts. This study suggests new trends for quickly computing reducts.

当前，属性约简作为粗糙集领域中最重要的主题之一，已经从不同的角度得到了广泛的探索。为了获得在属性约简中定义的合格归约，经常使用前向贪婪搜索。但是，先前的研究表明，如果数据量很大，这种搜索策略可能在计算上仍然很昂贵。有鉴于此，通过考虑属性之间的关系，提出了两个框架，旨在加快检索还原的过程。实际上，我们的考虑是分别基于属性之间的相似性和相似性来实现的。主要机制是：（1）对于基于差异的方法，在搜索还原过程中，具有显着差异的属性组合而不是一个，并且仅将一个属性添加到潜在还原中；（2）对于基于相似度的方法，与潜在还原中那些属性相似的候选属性将被暂时忽略，而不是在搜索还原过程中进行评估。在16个UCI数据集上的实验结果表明，无论是考虑单个粒度还是多个粒度属性约简，我们提出的方法不仅可以生成可能不会导致较差性能的归约，而且还可以提供卓越的计算归约时间效率。这项研究提出了快速计算减少量的新趋势。与潜在还原中那些属性相似的候选属性将被暂时忽略，而不是在搜索还原过程中进行评估。在16个UCI数据集上的实验结果表明，无论是考虑单个粒度还是多个粒度属性约简，我们提出的方法不仅可以生成可能不会导致较差性能的归约，而且还可以提供卓越的计算归约时间效率。这项研究提出了快速计算减少量的新趋势。与潜在还原中那些属性相似的候选属性将被暂时忽略，而不是在搜索还原过程中进行评估。在16个UCI数据集上的实验结果表明，无论是考虑单个粒度还是多个粒度属性约简，我们提出的方法不仅可以生成可能不会导致较差性能的归约，而且还可以提供卓越的计算归约时间效率。这项研究提出了快速计算减少量的新趋势。我们提出的方法不仅可以生成可能不会导致较差性能的归约，还可以提供卓越的计算归约时间效率。这项研究提出了快速计算减少量的新趋势。我们提出的方法不仅可以生成可能不会导致较差性能的归约，还可以提供卓越的计算归约时间效率。这项研究提出了快速计算减少量的新趋势。