Optimal Granularity Selection Method Based on Cost-sensitive Sequential Three-way Decisions

Qinghua ZHANG; Guohong PANG; Xintai LI; Xueqiu ZHANG

doi:10.11999/JEIT200821

Volume 43 Issue 10

Oct. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(10): 3001-3009

Qinghua ZHANG, Guohong PANG, Xintai LI, Xueqiu ZHANG. Optimal Granularity Selection Method Based on Cost-sensitive Sequential Three-way Decisions[J]. Journal of Electronics & Information Technology, 2021, 43(10): 3001-3009. doi: 10.11999/JEIT200821

Citation:

Qinghua ZHANG, Guohong PANG, Xintai LI, Xueqiu ZHANG. Optimal Granularity Selection Method Based on Cost-sensitive Sequential Three-way Decisions[J]. Journal of Electronics & Information Technology, 2021, 43(10): 3001-3009. doi: 10.11999/JEIT200821

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Optimal Granularity Selection Method Based on Cost-sensitive Sequential Three-way Decisions

doi: 10.11999/JEIT200821

Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Key Research and Development Program of China (2020YFC2003502), The National Natural Science Foundation of China (61876201)

Received Date: 2020-09-21
Rev Recd Date: 2021-07-19

Available Online: 2021-08-18

Publish Date: 2021-10-18

Abstract

Abstract

Optimal granularity selection is one of the hotspots in the research of sequential three-way decisions. It aims to solve complex problems through reasonable granularity selection. At present, in the field of optimal granularity selection, cost sensitivity is one of the important factors affecting decision making. To solve this problem, firstly, based on information gain and chi-squared test, a novel method to measure the attribute significance is proposed when constructing the multi-granularity space in this paper. Then, to better conform the practical application, the corresponding penalty rule is set by combining the cost parameters and the granularity, and the variation rule of the decision threshold is analyzed. Finally, to eliminate the influence of the dimensional difference between the test cost and the decision cost, an objective cost calculation method is given by the coefficient of variation. The experimental results show that the proposed algorithm can be used in existing cost cognition scene, and the optimal granular layer with the lowest cost can be obtained under the given cost scene.
- Sequential three-way decisions,
- Attribute significance,
- Penalty function,
- Coefficient of variation,
- Optimal granularity selection

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References(24)

References

[1]	LIAO Shujiao, ZHU Qingxin, QIAN Yuhua, et al. Multi-granularity feature selection on cost-sensitive data with measurement errors and variable costs[J]. Knowledge-Based Systems, 2018, 158: 25–42. doi: 10.1016/j.knosys.2018.05.020
[2]	YANG Jie, WANG Guoyin, ZHANG Qinghua, et al. Optimal granularity selection based on cost-sensitive sequential three-way decisions with rough fuzzy sets[J]. Knowledge-Based Systems, 2019, 163: 131–144. doi: 10.1016/j.knosys.2018.08.019
[3]	LI Huaxiong, ZHANG Libo, ZHOU Xianzhong, et al. Cost-sensitive sequential three-way decision modeling using a deep neural network[J]. International Journal of Approximate Reasoning, 2017, 85: 68–78. doi: 10.1016/j.ijar.2017.03.008
[4]	ZHANG Yanping, ZOU Huijin, CHEN Xi, et al. Cost-sensitive three-way decisions model based on CCA[C]. The 9th International Conference on Rough Sets and Current Trends in Computing, Granada and Madrid, Spain, 2014: 172–180. doi: 10.1007/978-3-319-08644-6_18.
[5]	JIA Xiuyi, LIAO Wenhe, TANG Zhenmin, et al. Minimum cost attribute reduction in decision-theoretic rough set models[J]. Information Sciences, 2013, 219: 151–167. doi: 10.1016/j.ins.2012.07.010
[6]	YANG Xibei, QI Yunsong, SONG Xiaoning, et al. Test cost sensitive multigranulation rough set: Model and minimal cost selection[J]. Information Sciences, 2013, 250: 184–199. doi: 10.1016/j.ins.2013.06.057
[7]	MIN Fan and LIU Qihe. A hierarchical model for test-cost-sensitive decision systems[J]. Information Sciences, 2009, 179(14): 2442–2452. doi: 10.1016/j.ins.2009.03.007
[8]	JU Hengrong, LI Huaxiong, YANG Xibei, et al. Cost-sensitive rough set: A multi-granulation approach[J]. Knowledge-Based Systems, 2017, 123: 137–153. doi: 10.1016/j.knosys.2017.02.019
[9]	JU Hengrong, YANG Xibei, YU Hualong, et al. Cost-sensitive rough set approach[J]. Information Sciences, 2016, 355/356: 282–298. doi: 10.1016/j.ins.2016.01.103
[10]	YAO Yiyu and DENG Xiaofei. Sequential three-way decisions with probabilistic rough sets[C]. IEEE 10th International Conference on Cognitive Informatics and Cognitive Computing (ICCI-CC’11), Banff, Canada, 2011: 120–125. doi: 10.1109/COGINF.2011.6016129.
[11]	ZHANG Qinghua, CHEN Yuhong, YANG Jie, et al. Fuzzy entropy: A more comprehensible perspective for interval shadowed sets of fuzzy sets[J]. IEEE Transactions on Fuzzy Systems, 2020, 28(11): 3008–3022. doi: 10.1109/tfuzz.2019.2947224
[12]	ZHANG Qinghua, ZHAO Fan, YANG Jie, et al. Three-way decisions of rough vague sets from the perspective of fuzziness[J]. Information Sciences, 2020, 523: 111–132. doi: 10.1016/j.ins.2020.03.013
[13]	张清华, 幸禹可, 周玉兰. 基于粒计算的增量式知识获取方法[J]. 电子与信息学报, 2011, 33(2): 435–441. doi: 10.3724/SP.J.1146.2010.00217 ZHANG Qinghua, XING Yuke, and ZHOU Yulan. The incremental knowledge acquisition algorithm based on granular computing[J]. Journal of Electronics &Information Technology, 2011, 33(2): 435–441. doi: 10.3724/SP.J.1146.2010.00217
[14]	FANG Yu, GAO Cong, and YAO Yiyu. Granularity-driven sequential three-way decisions: A cost-sensitive approach to classification[J]. Information Sciences, 2020, 507: 644–664. doi: 10.1016/j.ins.2019.06.003
[15]	LI Huaxiong, ZHANG Libo, HUANG Bing, et al. Sequential three-way decision and granulation for cost-sensitive face recognition[J]. Knowledge-Based Systems, 2016, 91: 241–251. doi: 10.1016/j.knosys.2015.07.040
[16]	QIAN Jin, LIU Caihui, MIAO Duoqian, et al. Sequential three-way decisions via multi-granularity[J]. Information Sciences, 2020, 507: 606–629. doi: 10.1016/j.ins.2019.03.052
[17]	陈泽华, 马贺. 基于粒矩阵的多输入多输出真值表快速并行约简算法[J]. 电子与信息学报, 2015, 37(5): 1260–1265. doi: 10.11999/JEIT141129 CHEN Zehua and MA He. Granular matrix based rapid parallel reduction algorithm for MIMO truth table[J]. Journal of Electronics &Information Technology, 2015, 37(5): 1260–1265. doi: 10.11999/JEIT141129
[18]	ZHANG Qinghua, XIA Deyou, and WANG Guoyin. Three-way decision model with two types of classification errors[J]. Information Sciences, 2017, 420: 431–453. doi: 10.1016/j.ins.2017.08.066
[19]	王国胤. Rough集理论与知识获取[M]. 西安: 西安交通大学出版社, 2001: 17–18.
[20]	PAWLAK Z. Rough sets[J]. International Journal of Computer & Information Sciences, 1982, 11(5): 341–356. doi: 10.1007/BF01001956
[21]	PLACKETT R L. Karl Pearson and the chi-squared test[J]. International Statistical Review, 1983, 51(1): 59–72. doi: 10.2307/1402731
[22]	周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 75.
[23]	QUINLAN J R. Induction of decision trees[J]. Machine Learning, 1986, 1(1): 81–106. doi: 10.1023/A:1022643204877
[24]	LIU Dun, LI Tianrui, and LIANG Decui. Three-way decisions in dynamic decision-theoretic rough sets[C]. The 8th International Conference on Rough Sets and Knowledge Technology, Halifax, Canada, 2013: 291–301. doi: 10.1007/978-3-642-41299-8_28.