Abstract The paper presents the proposed research methodology, dedicated to the application of greedy heuristics as a way of gathering information about available features. Discovered knowledge, represented in the form of generated decision rules, was employed to support feature selection and reduction process for induction of decision rules with classical rough set approach. Observations were executed over input data sets discretised by several methods. Experimental results show that elimination of less relevant attributes through the proposed methodology led to inferring rule sets with reduced cardinalities, while maintaining rule quality necessary for satisfactory classification.

中文翻译：

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基于启发式的粗糙集特征选择

摘要 本文提出了所提出的研究方法，致力于将贪婪启发式应用作为收集有关可用特征的信息的一种方式。发现的知识，以生成的决策规则的形式表示，被用来支持特征选择和减少过程，以通过经典的粗糙集方法归纳决策规则。对通过多种方法离散化的输入数据集执行观察。实验结果表明，通过所提出的方法消除不太相关的属性导致推断规则集的基数减少，同时保持令人满意的分类所需的规则质量。