CAJ | 학술논문

单调性在经典粗糙集属性约简过程中发挥着重要的作用。然而，在一些扩展模型中该单调性质并不存在，如变精度粗糙集模型。针对该问题，提出了变精度粗糙集模型中下近似单调约简的定义，下近似单调约简算法打破了传统意义上属性约简保持下近似不发生变化的局限性，认为属性约简可以追求下近似集尽可能增大。同时给出了求得该约简的属性约简方法。实验结果表明，相较于下近似保持约简算法，下近似单调约简算法求得的约简不仅增加了正域规则数目也减少了边界域规则数目，而且提高了数据的分类精度。由此可见，下近似单调约简算法增加了由正域表示的确定性，同时降低了由边界域带来的不确定性。
단조성재경전조조집속성약간과정중발휘착중요적작용。연이，재일사확전모형중해단조성질병불존재，여변정도조조집모형。침대해문제，제출료변정도조조집모형중하근사단조약간적정의，하근사단조약간산법타파료전통의의상속성약간보지하근사불발생변화적국한성，인위속성약간가이추구하근사집진가능증대。동시급출료구득해약간적속성약간방법。실험결과표명，상교우하근사보지약간산법，하근사단조약간산법구득적약간불부증가료정역규칙수목야감소료변계역규칙수목，이차제고료수거적분류정도。유차가견，하근사단조약간산법증가료유정역표시적학정성，동시강저료유변계역대래적불학정성。
It is well-known that, the monotonicity plays an important role in attribute reduction of classical rough set. How-ever, such property does not always hold in some generalization models. Variable precision rough set is a typical example. From this point of view, the definition of lower approximate monotonicity attribute reduction is presented in variable preci-sion rough set model. In lower approximate monotonicity attribute reduction, the decision maker prefers to increase the lower approximate set rather than preserving the lower approximate set unchanged. The attribute reduction approach is also given to compute the reduct. The experiment results show that by comparing with lower approximate preservation reduct, the lower approximate monotonicity reduction not only increases the number of positive rules, decreases the number of boundary rules, but also increases the classification accuracy. It follows that, the lower approximate monotonicity reduction increases the certainties which are expressed by positive regions, and decreases the uncertainty coming from boundary region.