CAJ | 학술논문

目前有很多粗糙集的推广模型通过引入参数的方法处理含有噪音的实际问题。基于粗糙集推广模型的约简算法可以发现保持信息含量不变的最小属性子集，但是其明显的不足是计算不同参数上的约简时，每次都要从头开始执行。将嵌套结构的理论结果应用于k-近邻模糊粗糙集的快速约简算法设计中，并利用嵌套结构，设计了一个基于已有约简的快速约简算法。该算法的特点是在参数改变时，不必重新运行经典的算法，而是利用已有的约简来计算新的约简。数值实验验证了快速约简算法可以显著地节省运行时间，表明了该算法的可行性和有效性。
목전유흔다조조집적추엄모형통과인입삼수적방법처리함유조음적실제문제。기우조조집추엄모형적약간산법가이발현보지신식함량불변적최소속성자집，단시기명현적불족시계산불동삼수상적약간시，매차도요종두개시집행。장감투결구적이론결과응용우k-근린모호조조집적쾌속약간산법설계중，병이용감투결구，설계료일개기우이유약간적쾌속약간산법。해산법적특점시재삼수개변시，불필중신운행경전적산법，이시이용이유적약간래계산신적약간。수치실험험증료쾌속약간산법가이현저지절성운행시간，표명료해산법적가행성화유효성。
Now a lot of generalized models of rough set are proposed by introducing some parameters to deal with the problems with noise. Traditional reduction algorithms are designed to find the minimum subset which keeps the information invariant. However, there is an obvious weakness that the algorithms have to be executed from the beginning on different parameters. This paper introduces the theoretical results of nested structure into the robust fuzzy rough set (i.e., k-nearest neighbor fuzzy rough sets), and then designs a fast reduction algorithm based on given reduction by using the nested structure. The main contribution of the proposed algorithm is that it can quickly find a reduction on different parameters when one reduction on certain parameter is already given. The numerical experiments verify that the executing time can be significantly saved through using fast reduction algorithm and demonstrate that the proposed algorithm is feasible and effective.