CCA三支决策模型的边界域样本处理

构造性覆盖算法（constructive covering algorithm，CCA）三支决策模型在学习过程中根据样本分布特征，自动产生正域、负域和边界域。该模型为边界域样本处理问题提供了新的思路。重点讨论了基于CCA的三支决策的边界域样本处理问题。对边界域样本处理提出了两种决策方案：一种为处理全部的边界域样本，给出了距中心最近原则、距边界最近原则、万有引力原则3种方法；另一种为处理部分的边界域样本，即只对满足一定条件的边界域样本作进一步的划分，这样使不满足条件的边界域样本仍保留在边界域，提高了边界域样本处理的正确率。用十字交叉法在5组数据集上对这两种决策方案进行了对比，实验结果表明，处理部分边界域样本时正确率更高，效果更好。

Dealing with Samples in Boundary Regions of Three-Way Decisions Model Based on CCA

The three-way decisions model based on constructive covering algorithm （CCA） produces automatically three regions：positive regions （POS）, negative regions （NEG） and boundary regions （BND） according to the distri-bution characteristic of samples. The model provides a new idea to deal with samples in BND for the theory of three-way decisions. This paper proposes two methods to deal with samples in BND：The first method deals with all samples in BND, which gives three principles, nearest to the center principle, nearest to the boundary principle and gravity principle;The second one deals with part of samples in BND, i.e., the samples which satisfy some condi-tions are dealt with, and the rest samples are still in the BND. The second method promotes the accuracy. This paper compares the two methods on five datasets using 10-fold cross-validation. The experimental results show that the effect and accuracy of dealing with part of samples in BND are better than those of dealing with all samples in BND.