Feature Subset Selection for Multi-scale Neighborhood Decision Information System
ZHANG Lujing1,2, LIN Guoping1,2, LIN Yidong1, KOU Yi1
1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000; 2. Fujian Key Laboratory of Granular Computing and Applications, Minnan Normal University, Zhangzhou 363000
Abstract：Feature subset selection for multi-scale decision information system is an effective data preprocessing method for multi-scale classification problems. However, data types are often diverse and mixed in real application. The existing multi-scale models cannot handle these data effectively. To solve this problem, a formal definition of multi-scale neighborhood radius for multi-source heterogeneous multi-scale data is proposed in this paper. Multi-scale neighborhood information granule is constructed and its related properties are studied. Attribute significance is discussed, and a feature subset selection algorithm is proposed. Optimal scale selection and feature selection are conducted synchronously. By improving the Wu-Leung model, the scope of its application in practical problems is expanded to some extent. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified on UCI datasets.
[1] ZADEH L A.Toward a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems, 1997, 90(2): 111-127. [2] YAO Y Y.Three-Way Decision and Granular Computing. International Journal of Approximate Reasoning, 2018, 103: 107-123. [3] ZADEH L A.Fuzzy Sets and Information Granularity // ZADEH L A, ed. Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems. Toh Tuck Link, Singapore: World Scientific Publishing, 1996: 433-448. [4] PAWLAK Z. Rough Sets. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356. [5] 胡清华,于达仁,谢宗霞.基于邻域粒化和粗糙逼近的数值属性约简.软件学报, 2008, 19(3): 640-649. (HU Q H, YU D R, XIE Z X.Numerical Attribute Reduction Based on Neighborhood Granulation and Rough Approximation. Journal of Software, 2008, 19(3): 640-649.) [6] HU Q H, YU D R, LIU J F, et al. Neighborhood Rough Set Based Heterogeneous Feature Subset Selection. Information Sciences, 2008, 178(18): 3577-3594. [7] CHEN H M, LI T R, CAI Y, et al. Parallel Attribute Reduction in Dominance-Based Neighborhood Rough Set. Information Sciences, 2016, 373: 351-368. [8] HU M, TSANG E C C, GUO Y T, et al. A Novel Approach to Attribute Reduction Based on Weighted Neighborhood Rough Sets. Knowledge-Based Systems, 2021, 220. DOI: 10.1016/j.knosys.2021.106908. [9] WU W Z, LEUNG Y.Theory and Applications of Granular Labelled Partitions in Multi-scale Decision Tables. Information Sciences, 2011, 181(18): 3878-3897. [10] GU S M, WU W Z.Knowledge Acquisition in Inconsistent Multi-scale Decision Systems // Proc of the International Conference on Rough Sets and Knowledge Technology. Berlin, Germany: Springer, 2011: 669-678. [11] WU W Z, LEUNG Y.Optimal Scale Selection for Multi-scale Decision Tables. International Journal of Approximate Reasoning, 2013, 54(8): 1107-1129. [12] SHE Y H, LI J H, YANG H L.A Local Approach to Rule Induction in Multi-scale Decision Tables. Knowledge-Based Systems, 2015, 89: 398-410. [13] WU W Z, QIAN Y H, LI T J, et al. On Rule Acquisition in Incomplete Multi-scale Decision Tables. Information Sciences, 2017, 378: 282-302. [14] LI F, HU B Q.A New Approach of Optimal Scale Selection to Multi-scale Decision Tables. Information Sciences, 2017, 381: 193-208. [15] XU Y H, WU W Z, TAN A H.Optimal Scale Selections in Consistent Generalized Multi-scale Decision Tables // Proc of the International Joint Conference on Rough Sets. Berlin, Germany: Springer, 2017: 185-198. [16] WU W Z, LEUNG Y.A Comparison Study of Optimal Scale Combination Selection in Generalized Multi-scale Decision Tables. International Journal of Machine Learning and Cybernetics, 2020, 11(5): 961-972. [17] 吴伟志,孙钰,王霞,等.不协调广义多尺度决策系统的局部最优尺度组合选择.模式识别与人工智能, 2021, 34(8): 689-700. (WU W Z, SUN Y, WANG X, et al. Local Optimal Scale Combination Selections in Inconsistent Generalized Multi-scale Decision Systems. Pattern Recognition and Artificial Intelligence, 2021, 34(8): 689-700.) [18] 王金波,吴伟志.基于证据理论的广义多尺度覆盖决策系统的最优尺度组合.模式识别与人工智能, 2022, 35(4): 291-305. (WANG J B, WU W Z.Evidence-Theory-Based Optimal Scale Combinations in Generalized Multi-scale Covering Decision Sys-tems. Pattern Recognition and Artificial Intelligence, 2022, 35(4): 291-305.) [19] 吴伟志,庄宇斌,谭安辉,等.不协调广义多尺度决策系统的尺度组合.模式识别与人工智能, 2018, 31(6): 485-494. (WU W Z, ZHUANG Y B, TAN A H, et al. Scale Combinations in Inconsistent Generalized Multi-scale Decision Systems. Pattern Recognition and Artificial Intelligence, 2018, 31(6): 485-494.) [20] 张嘉茹,吴伟志,杨烨.协调广义决策多尺度序信息系统的知识获取.模式识别与人工智能, 2022, 35(9): 789-804. (ZHANG J R, WU W Z, YANG Y.Knowledge Acquisition for Consistent Generalized Decision Multi-scale Ordered Information Systems. Pattern Recognition and Artificial Intelligence, 2022, 35(9): 789-804.) [21] 牛东苒,吴伟志,李同军.广义多尺度决策系统中基于可变精度的最优尺度组合.模式识别与人工智能, 2019, 32(11): 965-974. (NIU D R, WU W Z, LI T J.Variable Precision Based Optimal Scale Combinations in Generalized Multi-scale Decision Systems. Pattern Recognition and Artificial Intelligence, 2019, 32(11): 965-974.) [22] HUANG Z H, LI J J, DAI W Z, et al. Generalized Multi-scale Decision Tables with Multi-scale Decision Attributes. International Journal of Approximate Reasoning, 2019, 115: 194-208. [23] HUANG Z H, LI J J.Multi-scale Covering Rough Sets with Applications to Data Classification. Applied Soft Computing, 2021, 110. DOI: 10.1016/j.asoc.2021.107736. [24] LI W K, LI J J, HUANG J X, et al. A New Rough Set Model Based on Multi-scale Covering. International Journal of Machine Learning and Cybernetics, 2021, 12(1): 243-256. [25] ZHENG J W, WU W Z, BAO H, et al. Evidence Theory Based Optimal Scale Selection for Multi-scale Ordered Decision Systems. International Journal of Machine Learning and Cybernetics, 2022, 13(4): 1115-1129. [26] WANG J B, WU W Z, TAN A H.Multi-Granulation-Based Know-ledge Discovery in Incomplete Generalized Multi-scale Decision Systems. International Journal of Machine Learning and Cybernetics, 2022, 13(12): 3963-3979. [27] HAO C, LI J H, FAN M, et al. Optimal Scale Selection in Dynamic Multi-scale Decision Tables Based on Sequential Three-Way Decisions. Information Sciences Optimal Scale Selection in Dynamic Multi-scale Decision Tables Based on Sequential Three-Way Decisions. Information Sciences, 2017, 415/416: 213-232. [28] LI J H, FENG Y.Update of Optimal Scale in Dynamic Multi-scale Decision Information Systems. International Journal of Approximate Reasoning, 2023, 152: 310-324. [29] CHEN Y S, LI J H, LI J J, et al. Sequential 3WD-Based Local Optimal Scale Selection in Dynamic Multi-scale Decision Information Systems. International Journal of Approximate Reasoning, 2023, 152: 221-235. [30] 陈应生,李进金,林荣德,等.多尺度集值决策信息系统.控制与决策, 2022, 37(2): 455-463. (CHEN Y S, LI J J, LIN R D, et al. Multi-scale Set Valued Decision Information System. Control and Decision, 2022, 37(2): 455-463.) [31] 陈应生,李进金,林荣德,等.多尺度覆盖决策信息系统的布尔矩阵方法.模式识别与人工智能, 2020, 33(9): 776-785. (CHEN Y S, LI J J, LIN R D, et al. Boolean Matrix Approach for Multi-scale Covering Decision Information System. Pattern Recognition and Artificial Intelligence, 2020, 33(9): 776-785.) [32] HUANG B, WU W Z, YAN J J, et al. Inclusion Measure-Based Multi-granulation Decision-Theoretic Rough Sets in Multi-scale Intuitionistic Fuzzy Information Tables. Information Sciences, 2020, 507: 421-448. [33] HUANG B, LI H X, FENG G F, et al. Dominance-Based Rough Sets in Multi-scale Intuitionistic Fuzzy Decision Tables. Applied Mathematics and Computation, 2019, 348: 487-512. [34] HUANG Z H, LI J J.Feature Subset Selection with Multi-scale Fuzzy Granulation. IEEE Transactions on Artificial Intelligence, 2022. DOI: 10.1109/TAI.2022.3144242. [35] 杨璇,黄兵.多尺度决策系统中基于模糊相似关系的决策粗糙集最优尺度选择与约简.南京理工大学学报, 2021, 45(4): 455-463. (YANG X, HUANG B.Optimal Scale Selection and Reduction of Decision-Theoretic Rough Set Based on Fuzzy Similarity Relation in Multi-scale Decision Systems. Journal of Nanjing University of Science and Technology, 2021, 45(4): 455-463.) [36] WAN J H, CHEN H M, YUAN Z, et al. A Novel Hybrid Feature Selection Method Considering Feature Interaction in Neighborhood Rough Set. Knowledge-Based Systems, 2021, 227. DOI: 10.1016/j.knosys.2021.107167. [37] LI F, HU B Q, WANG J.Stepwise Optimal Scale Selection for Multi-scale Decision Tables via Attribute Significance. Knowledge-Based Systems, 2017, 129: 4-16.
2023, Vol. 36 
