Stańczyk Urszula
Department of Computer Graphics, Vision and Digital Systems, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland.
Entropy (Basel). 2022 Nov 3;24(11):1602. doi: 10.3390/e24111602.
Methods and techniques of feature selection support expert domain knowledge in the search for attributes, which are the most important for a task. These approaches can also be used in the process of closer tailoring of the obtained solutions when dimensionality reduction is aimed not only at variables but also at learners. The paper reports on research where attribute rankings were employed to filter induced decision rules. The rankings were constructed through the proposed weighting factor based on the concept of decision reducts-a feature reduction mechanism embedded in the rough set theory. Classical rough sets operate only in discrete input space by indiscernibility relation. Replacing it with dominance enables processing real-valued data. Decision reducts were found for both numeric and discrete attributes, transformed by selected discretisation approaches. The calculated ranking scores were used to control the selection of decision rules. The performance of the resulting rule classifiers was observed for the entire range of rejected variables, for decision rules with conditions on continuous values, discretised conditions, and also inferred from discrete data. The predictive powers were analysed and compared to detect existing trends. The experiments show that for all variants of the rule sets, not only was dimensionality reduction possible, but also predictions were improved, which validated the proposed methodology.
特征选择的方法和技术在寻找对任务最为重要的属性时支持专家领域知识。当降维不仅针对变量而且针对学习器时，这些方法也可用于对所得解决方案进行更精细调整的过程。本文报道了一项研究，其中使用属性排名来过滤归纳出的决策规则。这些排名是通过基于决策约简概念提出的加权因子构建的，决策约简是粗糙集理论中嵌入的一种特征约简机制。经典粗糙集仅通过不可分辨关系在离散输入空间中运行。用支配关系取代它可以处理实值数据。通过选定的离散化方法对数值属性和离散属性都找到了决策约简。计算出的排名分数用于控制决策规则的选择。对于被拒绝变量的整个范围、具有连续值条件的决策规则、离散条件的决策规则以及从离散数据推断出的决策规则，都观察了所得规则分类器的性能。分析并比较了预测能力以检测现有趋势。实验表明，对于规则集的所有变体，不仅可以进行降维，而且预测得到了改进，这验证了所提出的方法。
