Table of Contents

• Decision-Oriented Rough Set MethodsJiye Liang. 3-12 [doi]
• On Generalized Decision Functions: Reducts, Networks and EnsemblesDominik Slezak. 13-23 [doi]
• Formalization of Medical Diagnostic RulesShusaku Tsumoto, Shoji Hirano. 24-35 [doi]
• Multi-granularity Intelligent Information ProcessingGuoyin Wang, Ji Xu, Qinghua Zhang, Yuchao Liu. 36-48 [doi]
• Granular Structures Induced by Interval Sets and Rough SetsNing Zhong, Jiajin Huang. 49-60 [doi]
• Empirical Risk Minimization for Variable Consistency Dominance-Based Rough Set ApproachJerzy Blaszczynski, Yoshifumi Kusunoki, Masahiro Inuiguchi, Roman Slowinski. 63-72 [doi]
• Rough Set Approximations in Multi-scale Interval Information SystemsShen-Ming Gu, Ya-Hong Wan, Wei-Zhi Wu, Tong-Jun Li. 73-81 [doi]
• A New Subsystem-Based Definition of Generalized Rough Set ModelCaihui Liu, Meizhi Wang, Yanfei Dai, Yueli Luo. 82-89 [doi]
• A Comparison of Two Types of Covering-Based Rough Sets Through the Complement of CoveringsYanfang Liu, William Zhu. 90-101 [doi]
• On the Nearness Measures of Near SetsKeyun Qin, Bo Li. 102-111 [doi]
• Topological Properties for Approximation Operators in Covering Based Rough SetsMauricio Restrepo, Jonatan Gómez. 112-123 [doi]
• Preclusivity and Simple GraphsGiampiero Chiaselotti, Davide Ciucci, Tommaso Gentile, Federico Infusino. 127-137 [doi]
• Preclusivity and Simple Graphs: The n-cycle and n-path CasesGiampiero Chiaselotti, Davide Ciucci, Tommaso Gentile, Federico Infusino. 138-148 [doi]
• Connectedness of Graph and Matroid by Covering-Based Rough SetsHui Li, William Zhu. 149-160 [doi]
• Controllability in Directed Complex Networks: Granular Computing PerspectiveYunyun Yang, Gang Xie, Zehua Chen. 161-169 [doi]
• Dynamic Maintenance of Rough Fuzzy Approximations with the Variation of Objects and AttributesYanyong Huang, Tianrui Li, Shi-Jinn Horng. 173-184 [doi]
• Semi-Supervised Fuzzy-Rough Feature SelectionRichard Jensen, Sarah Vluymans, Neil Mac Parthaláin, Chris Cornelis, Yvan Saeys. 185-195 [doi]
• Modified Generalised Fuzzy Petri Nets for Rule-Based SystemsZbigniew Suraj. 196-206 [doi]
• Fuzzy Rough Decision Trees for Multi-label ClassificationXiaoxue Wang, Shuang An, Hong Shi, Qinghua Hu. 207-217 [doi]
• I I -Intuitionistic Fuzzy Rough Approximation OperatorsWei-Zhi Wu, You-Hong Xu, Tong-Jun Li, Xia Wang. 218-229 [doi]
• Knowledge Supported Refinements for Rough Granular Computing: A Case of Life Insurance IndustryKao-Yi Shen, Gwo-Hshiung Tzeng. 233-244 [doi]
• Building Granular Systems - from Concepts to ApplicationsMarcin S. Szczuka, Andrzej Jankowski, Andrzej Skowron, Dominik Slezak. 245-255 [doi]
• The Rough Granular Approach to Classifier Synthesis by Means of SVMJacek Szypulski, Piotr Artiemjew. 256-263 [doi]
• The Boosting and Bootstrap Ensemble for Classifiers Based on Weak Rough InclusionsPiotr Artiemjew. 267-277 [doi]
• Extraction of Off-Line Handwritten Characters Based on a Soft K-Segments for Principal CurvesNa Jiao. 278-285 [doi]
• A Knowledge Acquisition Model Based on Formal Concept Analysis in Complex Information SystemsXiangping Kang, Duoqian Miao, Na Jiao. 286-297 [doi]
• The Borda Count, the Intersection and the Highest Rank Method in a Dispersed Decision-Making SystemMalgorzata Przybyla-Kasperek. 298-309 [doi]
• An Ensemble Learning Approach Based on Missing-Valued TablesSeiki Ubukata, Taro Miyazaki, Akira Notsu, Katsuhiro Honda, Masahiro Inuiguchi. 310-321 [doi]
• Multiple-Side Multiple-Learner for Incomplete Data ClassificationYuan-ting Yan, Yan-ping Zhang, Xiu-Quan Du. 322-331 [doi]
• Sparse Matrix Feature Selection in Multi-label LearningWenyuan Yang, Bufang Zhou, William Zhu. 332-339 [doi]
• A Classification Method for Imbalanced Data Based on SMOTE and Fuzzy Rough Nearest Neighbor AlgorithmWeibin Zhao, Mengting Xu, Xiuyi Jia, Lin Shang. 340-351 [doi]
• Region Vector Based Attribute Reducts in Decision-Theoretic Rough SetsGuoshun Huang, Yiyu Yao. 355-365 [doi]
• How to Evaluate Three-Way Decisions Based Binary Classification?Xiuyi Jia, Lin Shang. 366-375 [doi]
• A Moderate Attribute Reduction Approach in Decision-Theoretic Rough SetHengrong Ju, Xibei Yang, Pei Yang, Huaxiong Li, Xianzhong Zhou. 376-388 [doi]
• Decisions Tree Learning Method Based on Three-Way DecisionsYangyang Liu, Jiucheng Xu, Lin Sun, Lina Du. 389-400 [doi]
• Multi-decision-makers-based Monotonic Variable Consistency Rough Set Approach with Multiple Attributes and CriteriaWenBin Pei, He Lin, Li Li. 401-413 [doi]
• Determining Three-Way Decision Regions by Combining Gini Objective Functions and GTRSYan Zhang, Jingtao Yao. 414-425 [doi]
• Mining Data from Coal Mines: IJCRS'15 Data ChallengeAndrzej Janusz, Marek Sikora, Lukasz Wróbel, Sebastian Stawicki, Marek Grzegorowski, Piotr Wojtas, Dominik Slezak. 429-438 [doi]
• Prediction of Methane Outbreak in Coal Mines from Historical Sensor Data under Distribution DriftMarc Boullé. 439-451 [doi]
• Window-Based Feature Engineering for Prediction of Methane Threats in Coal MinesMarek Grzegorowski, Sebastian Stawicki. 452-463 [doi]
• SVM Parameter Tuning with Grid Search and Its Impact on Reduction of Model Over-fittingPetre Lameski, Eftim Zdravevski, Riste Mingov, Andrea Kulakov. 464-474 [doi]
• Detecting Methane Outbreaks from Time Series Data with Deep Neural NetworksKrzysztof Pawlowski, Karol Kurach. 475-484 [doi]
• Self-Organized Predictor of Methane Concentration Warnings in Coal MinesDymitr Ruta, Ling Cen. 485-493 [doi]
• Prediction of Methane Outbreaks in Coal Mines from Multivariate Time Series Using Random ForestAdam Zagorecki. 494-500 [doi]