The need to measure bias encoded in tabular data that are used to solve pattern recognition problems is widely recognized by academia, legislators and enterprises alike. In previous work, we proposed a bias quantification measure, called fuzzy-rough uncer-tainty, which relies on the fuzzy-rough set theory. The intuition dictates that protected features should not change the fuzzy-rough boundary regions of a decision class significantly. The extent to which this happens is a proxy for bias expressed as uncertainty in adecision-making context. Our measure's main advantage is that it does not depend on any machine learning prediction model but adistance function. In this paper, we extend our study by exploring the existence of bias encoded implicitly in non-protected featuresas defined by the correlation between protected and unprotected attributes. This analysis leads to four scenarios that domain experts should evaluate before deciding how to tackle bias. In addition, we conduct a sensitivity analysis to determine the fuzzy operatorsand distance function that best capture change in the boundary regions.
Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）；School of Artificial Intelligence and Computer Science, Nantong University（南通大学人工智能与计算机科学学院）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Department of Electrical and Computer Engineering, University of Alberta（阿尔伯塔大学电气与计算机工程系）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Department of Electrical and Computer Engineering, King Abdulaziz University（阿卜杜勒阿齐兹大学电气与计算机工程系）；Department of Computer Engineering, Istinye University（伊斯提尼耶大学计算机工程系）
Consistency-guided semi-supervised outlier detection in heterogeneous data using fuzzy rough sets
College of Computer Science, Sichuan University（四川大学计算机科学学院）；Department of Computer Science and Operations Research, University of Montreal（蒙特利尔大学计算机科学与运筹学系）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机科学与人工智能学院）
Department of Electrical and Computer Engineering, University of Alberta（阿尔伯塔大学电气与计算机工程系）；Department of Computer Science and Technology, Nanjing University（南京大学计算机科学与技术系）；School of Computer, Jiangsu University of Science and Technology（江苏科技大学计算机学院）；School of Artificial Intelligence and Computer Science, Nantong University（南通大学人工智能与计算机科学学院）
A Fuzzy-Rough based Binary Shuffled Frog Leaping Algorithm for Feature Selection
Memorial University of Newfoundland（纽芬兰纪念大学）；University of Windsor（温莎大学）；Shahid Bahonar University of Kerman（克尔曼沙希德·巴霍纳尔大学）；Gwangju Institute of Science and Technology（光州科学技术院）
特征选择和属性约简是机器学习、数据采掘和模式识别领域中的关键问题，并且被广泛使用的技术通过选择特征的子集或去除不相关的特征来克服众所周知的Curse of Precisionality现象。本文提出了一种新的特征选择方法，有效地进行属性约简，从而选择数据集的信息量最大的特征。它由两个部分组成：1）一个功能子集评估的措施，和2）一个搜索策略。对于评价指标，我们采用了模糊粗糙依赖度（FRFDD）在基于下近似的模糊粗糙特征选择（L-FRFS），由于其有效性在特征选择。在搜索策略方面，提出了一种新的二进制混合蛙跳算法（B-SFLA）。将B-SFLA与FRDD相结合，得到了一种新的特征选择方法。非参数统计测试进行比较所提出的方法与现有的几种方法超过22个数据集，包括9个高维和大型的，从UCI存储库。实验结果表明，B-SFLA方法显着优于其他元启发式方法的选择功能的数量和分类精度。
