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Decision-theoretic rough set: A multicost strategy

机译：决策理论粗糙集：多成本策略

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摘要

By introducing the misclassification and delayed decision costs into the probabilistic approximations of the target, the model of decision-theoretic rough set is then sensitive to cost. However, traditional decision-theoretic rough set is proposed based on one and only one cost matrix, such model does not take the characteristics of multiplicity and variability of cost into consideration. To fill this gap, a multicost strategy is developed for decision-theoretic rough set. Firstly, from the viewpoint of the voting fusion mechanism, a parameterized decision-theoretic rough set is proposed. Secondly, based on the new model, the smallest possible cost and the largest possible cost are calculated in decision systems. Finally, both the decision-monotocity and cost criteria are introduced into the attribute reductions. The heuristic algorithm is used to compute decision-monotonicity reduct while the genetic algorithm is used to compute the smallest and the largest possible cost reducts. Experimental results on eight UCI data sets tell us: I. compared with the raw data, decision-monotocity reduct can generate greater lower approximations and more decision rules; 2. the smallest possible cost reduct is much better than decision-monotocity reduct for obtaining smaller costs and more decision rules. This study suggests new research trends concerning decision-theoretic rough set theory. (C) 2015 Elsevier B.V. All rights reserved.
机译：通过将错误分类和延迟的决策成本引入目标的概率近似中，决策理论粗糙集模型随后对成本敏感。然而，传统的决策理论粗糙集是基于一个成本矩阵和一个成本矩阵提出的，该模型没有考虑成本的多样性和可变性。为了填补这一空白，针对决策理论的粗糙集开发了一种多成本策略。首先，从投票融合机制的角度出发，提出了一种参数化决策理论粗糙集。其次，基于新模型，在决策系统中计算了最小可能的成本和最大可能的成本。最后，将决策唯一性和成本标准都引入到属性约简中。启发式算法用于计算决策单调性减少量，而遗传算法用于计算最小和最大可能的成本减少量​​。在八个UCI数据集上的实验结果告诉我们：I.与原始数据相比，决策唯一性约简可以产生更大的更低近似值和更多决策规则； 2.为了获得更小的成本和更多的决策规则，最小的可能成本降低要比决策垄断性降低要好得多。这项研究提出了有关决策理论粗糙集理论的新研究趋势。 （C）2015 Elsevier B.V.保留所有权利。

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  • 来源
    《Knowledge-Based Systems》 |2016年第1期|71-83|共13页
  • 作者

    Dou Huili; Yang Xibei; Song Xiaoning; Yu Hualong; Wu Wei-Zhi; Yang Jingyu;

  • 作者单位

    Jiangsu Univ Sci & Technol, Sch Comp Sci & Engn, Zhenjiang 212003, Jiangsu, Peoples R China;

    Jiangsu Univ Sci & Technol, Sch Comp Sci & Engn, Zhenjiang 212003, Jiangsu, Peoples R China|Nanjing Univ Sci & Technol, Sch Econ & Management, Nanjing 210094, Jiangsu, Peoples R China;

    Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China;

    Jiangsu Univ Sci & Technol, Sch Comp Sci & Engn, Zhenjiang 212003, Jiangsu, Peoples R China;

    Zhejiang Ocean Univ, Key Lab Oceanog Big Data Min & Applicat Zhejiang, Zhoushan 316022, Peoples R China;

    Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Jiangsu, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cost reduct; Decision-monotocity reduct; Decision-theoretic rough set; Multiple cost matrices;

    机译：成本削减;决策单例削减;决策理论粗糙集;多个成本矩阵;

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