南京大学学报（自然科学版）2019，Vol.55Issue(4)：529-536,8.DOI:10.13232/j.cnki.jnju.2019.04.002
基于极大相容块的邻域粗糙集模型
Neighborhood rough set model based on maximal consistent blocks
摘要
Abstract
For numerical data,neighborhood rough set model is an effective tool for dealing with uncertain information. The existing neighborhood rough set models only focus on the consistent situation that all samples in the neighborhood are in a single decision class. So they cannot make use of the information contained in the boundary samples with multiple decision class labels in the neighborhood. Aiming at this limitation of neighborhood rough set mode, we combine the concept of maximal consistent block of a tolerance relation with neighborhood rough set model and select the largest equivalent block in a sample neighborhood as the minimum information granule. We establish a new model, called neighborhood rough set model based on maximal consistent block,by redefining some concepts,such as upper and lower approximations, attribute importance and so on. The new model can enlarge the positive region by transforming the boundary samples into consistent samples in smaller information granules. In addition, we construct the corresponding attribute reduction algorithm by using the forward greedy strategy. The effectiveness of the proposed model is validated by the experiments on seven public UCI data sets.关键词
属性约简/边界样本/邻域粗糙集/极大相容块Key words
attribute reduction/boundary sample/neighborhood rough set/maximal consistent block分类
信息技术与安全科学引用本文复制引用
程永林,李德玉,王素格..基于极大相容块的邻域粗糙集模型[J].南京大学学报（自然科学版）,2019,55(4):529-536,8.基金项目
国家自然科学基金(61672331,61573231,61432011,61806116),山西省重点研发计划(201803D421024),山西省自然科学基金(201801D221175),山西省高等学校科技创新项目(201802014) （61672331,61573231,61432011,61806116）
