三支决策与决策粗糙集融合模型

Paw lak粗糙集模型没有对正域、边界域和负域赋予语义,不能进行再决策,而三支决策对边界域赋予了新的语义,可以对边界域做出进一步刻画,对于边界域的进一步划分,依据属性的重要性,使满足条件的样本划入再决策域,不满足条件的样本继续保留在边界域中,降低了边界域样本处理的失误率.本文在对概率粗糙集模型、三支决策粗糙集的理论、贝叶斯理论的决策过程和决策粗糙集模型进行研究的基础上,提出了一种三支决策与决策粗糙集融合模型,与Paw lak-三支决策模型相比,其划分损失更小,处理结果更优.该模型运用三支决策理论对决策粗糙集的边界域赋予延迟决策的语义,对于延迟决策再运用三支决策理论进行迭代操作,对边界域样本进一步处理.在迭代的过程中,依据属性的重要程度将属性排序,从而客观的得到迭代过程中每次优先依据哪个属性进行划分.实验结果表明,该模型比单一运用决策粗糙集模型进行决策代价小,三支决策通过迭代对边界域处理的正确率有所提高,这为准确决策提供了一种新的方法.

Merging Three-Way Decisions with Decision-Theoretic Rough Sets

Paw lak rough set model was lacking in giving semantic to positive regions, negative regions and boundary regions. The boundary could not make decision again. But three-way decisions gave a new semantic to boundary regions and we can deal with samples in boundary regions. Based on importance of attribute, samples which meet the conditions were delimit to decision region and others would be maintained in the boundary regions in order to reduce false positives when deal with samples in boundary regions. Based on the study of probabilistic rough set model, three-way decisions-theoretic rough set, Bayesian decision-making process and decisions-theoretic rough set model, this paper presents Three-way Decision mix Decision-Theoretic rough set model(TmD). Compared with the new model with Paw lak-three way decisions model, the loss of division of this model is smaller and the result is more reasonable. This model gives the boundary regions semantic which is delaying decisions. Using three-way decisions makes iterative operation for delaying decisions. In the process of iteration, attributes will be ordered based on the importance of the attribute, thus objectively get the attribute of priority being used in the process of iteration. Experimental results show that the model has a smaller decision cost than only using decision-theoretic rough sets and three-way decisions with iterative operation have a higher accuracy when deal with samples in boundary regions. This paper provides a new method for accurate decision-making.