Great Deluge Algorithm for Rough Set Attribute Reduction
Great Deluge Algorithm for Rough Set Attribute Reduction
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DOI：
10.1007/978-3-642-17622-7_19
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发表时间：
2010-12
期刊：
影响因子：
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通讯作者：
S. Abdullah;N. S. Jaddi
中科院分区：
文献类型：
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作者：
S. Abdullah;N. S. Jaddi
属性约简是从原始特征集中选择特征子集的过程，这些特征在给定数据集中形成模式。它可以被定义为一个消除冗余属性，同时能够避免任何信息丢失的过程，使得所选择的子集足以描述原始特征。在本文中，我们提出了一种用于粗糙集理论中属性约简的出色洪水算法（GD-RSAR）。大洪水是一种元启发式方法，对参数的依赖性较小。只需要两个参数； “花费”的时间和预期的最终解决方案。该算法总是接受改进的解决方案。如果比上边界值或“水平”更好，则最差的解决方案将被接受。 GD-RSAR 已在 UCI 提供的公共领域数据集上进行了测试。基准数据集上的实验结果表明，该方法是有效的，并且与以前的可用方法相比能够获得有竞争力的结果。还讨论了这种简单方法的可能扩展。
Attribute reduction is the process of selecting a subset of features from the original set of features that forms patterns in a given dataset. It can be defined as a process to eliminate redundant attributes and at the same time is able to avoid any information loss, so that the selected subset is sufficient to describe the original features. In this paper, we present a great deluge algorithm for attribute reduction in rough set theory (GD-RSAR). Great deluge is a meta-heuristic approach that is less parameter dependent. There are only two parameters needed; the time to “spend” and the expected final solution. The algorithm always accepts improved solutions. The worse solution will be accepted if it is better than the upper boundary value or “level”. GD-RSAR has been tested on the public domain datasets available in UCI. Experimental results on benchmark datasets demonstrate that this approach is effective and able to obtain competitive results compared to previous available methods. Possible extensions upon this simple approach are also discussed.