摘要:
在邻域粗糙集中，基于信息度量的属性约简具有重要应用意义.然而，条件邻域熵具有粒化非单调性，故其属性约简具有应用局限性.对此，采用粒计算技术及相关的3层粒结构,构建具有粒化单调性的条件邻域熵，进而研究其相关属性约简.首先，揭示条件邻域熵的粒化非单调性及其根源；其次，采用3层粒结构，自底向上构建一种新型条件邻域熵，获得其粒化单调性；进而，基于粒化单调的条件邻域熵，建立属性约简及启发式约简算法；最后，采用UCI(University of CaliforniaIrvine)数据实验，验证改进条件邻域熵的单调性与启发式约简算法的有效性.所得结果表明：新建条件邻域熵具有粒化单调性，改进了条件邻域熵，其诱导的属性约简具有应用前景.
Abstract:
In the neighborhood rough sets, the attribute reduction based on information measures holds fundamental research value and application significance. However, the conditional neighborhood entropy exhibits granulation non-monotonicity, so its attribute reduction has the research difficulty and application limitation. Aiming at this issue, by virtue of the granular computing technology and its relevant three-layer granular structure, a novel conditional neighborhood entropy with granulation monotonicity is constructed, and its relevant attribute reduction is further investigated. At first, the granulation non-monotonicity and its roots of the conditional neighborhood entropy are revealed; then, the three-layer granular structure is adopted to construct a new conditional neighborhood entropy by the bottom-up strategy, and the corresponding granulation monotonicity is gained; furthermore, relevant attribute reduction and its heuristic reduction algorithm are studied, according to this proposed information measure with the granulation monotonicity; finally, data experiments based on the UCI (University of CaliforniaIrvine) machine learning repository are implemented, and thus they verify both the granulation monotonicity of the constructed conditional neighborhood entropy and the calculation effectiveness of the related heuristic reduction algorithm. As shown by the obtained results, the established conditional neighborhood entropy has the granulation monotonicity to improve the conditional neighborhood entropy, and its induced attribute reduction has broad application prospects.
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