Attribute reduction is one of the biggest challenges encountered in computational intelligence, data mining, pattern recognition, and machine learning. Effective in feature selection as the rough set theory is, it can only handle symbolic attributes. In order to overcome this drawback, the fuzzy rough set model is proposed, which is an extended model of rough sets and is able to deal with imprecision and uncertainty in both symbolic and numerical attributes. The existing attribute selection algorithms based on the fuzzy rough set model mainly take the angle of “attribute set,” which means they define the object function representing the predictive ability for an attribute subset with regard to the domain of discourse, rather than following the view of an “object pair.” Algorithms from the viewpoint of the object pair can ignore the object pairs that are already discerned by the selected attribute subsets and, thus, need only to deal with part of object pairs instead of the whole object pairs from the discourse, which makes such algorithms more efficient in attribute selection. In this paper, we propose the concept of reduced maximal discernibility pairs, which directly adopts the perspective of the object pair in the framework of the fuzzy rough set model. Then, we develop two attribute selection algorithms, named as reduced maximal discernibility pairs selection and weighted reduced maximal discernibility pair selection, based on the reduced maximal discernibility pairs. Experiment results show that the proposed algorithms are effective and efficient in attribute selection.

中文翻译：

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基于最大可辨性对的模糊粗糙集属性约简方法

属性约简是计算智能、数据挖掘、模式识别和机器学习中遇到的最大挑战之一。粗糙集理论在特征选择方面很有效，它只能处理符号属性。为了克服这个缺点，提出了模糊粗糙集模型，它是粗糙集的扩展模型，能够处理符号和数值属性的不精确性和不确定性。现有的基于模糊粗糙集模型的属性选择算法主要是从“属性集”的角度出发，即定义了代表属性子集对话语域的预测能力的目标函数，而不是遵循视图的“对象对。” 从对象对的角度来看的算法可以忽略所选属性子集已经识别的对象对，因此只需要处理部分对象对而不是话语中的整个对象对，这使得这种算法更有效的属性选择。在本文中，我们提出了约简最大可辨别对的概念，它直接采用模糊粗糙集模型框架中对象对的视角。然后，我们开发了两种属性选择算法，分别称为缩减最大识别对选择和加权缩减最大识别对选择，基于缩减的最大识别对。实验结果表明，所提算法在属性选择方面是有效且高效的。