Cost-sensitive rough set: A multi-granulation approach

Ju, Hengrong; Li, Huaxiong; Yang, Xibei; Zhou, Xianzhong; Huang, Bing

doi:10.1016/j.knosys.2017.02.019

Cited by 66 publications

(12 citation statements)

References 59 publications

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“…Furthermore, the decision costs related to three different regions are shown in Table 5. With a careful investigation of Table 5, we notice that the reducing of total decision costs shown in Figure 4 [8,14,49]) and NON-NEG-Reduct (nonnegative region extension based attribute reduction [49]). The details are shown in Figure 5.…”

Section: Experiments On Raw Attributesmentioning

confidence: 99%

“…Different from classical rough set and its various generalizations [1][2][3], decision-theoretic rough set (DTRS) [4][5][6] has been demonstrated to be useful in many cost related problems [7][8][9][10][11]. Such model introduces not only the Bayesian decision but also the minimal risk into the construction of lower and upper approximations.…”

Section: Introductionmentioning

confidence: 99%

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Pseudolabel Decision-Theoretic Rough Set

Wang

Zhang

Liu

et al. 2019

Mathematical Problems in Engineering

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In decision-theoretic rough set (DTRS), the decision costs are used to generate the thresholds for characterizing the probabilistic approximations. Similar to other rough sets, many generalized DTRS can also be formed by using different binary relations. Nevertheless, it should be noticed that most of the processes for calculating binary relations do not take the labels of samples into account, which may lead to the lower discrimination; for example, samples with different labels are regarded as indistinguishable. To fill such gap, the main contribution of this paper is to propose a pseudolabel strategy for constructing new DTRS. Firstly, a pseudolabel neighborhood relation is presented, which can differentiate samples by not only the neighborhood technique but also the pseudolabels of samples. Immediately, the pseudolabel neighborhood decision-theoretic rough set (PLNDTRS) can be constructed. Secondly, the problem of attribute reduction is explored, which aims to further reduce the PLNDTRS related decision costs. A heuristic algorithm is also designed to find such reduct. Finally, the clustering technique is employed to generate the pseudolabels of samples; the experimental results over 15 UCI data sets tell us that PLNDTRS is superior to DTRS without using pseudolabels because the former can generate lower decision costs. Moreover, the proposed heuristic algorithm is also effective in providing satisfied reducts. This study suggests new trends concerning cost sensitivity problem in rough data analysis.

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Section: Experiments On Raw Attributesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pseudolabel Decision-Theoretic Rough Set

Wang

Zhang

Liu

et al. 2019

Mathematical Problems in Engineering

Self Cite

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show abstract

“…Attribute reduction is one of the key topics in rough-set theory [38]. Generally speaking, the purpose of attribute reduction is to delete the redundant attributes by some given constraints [39].…”

Section: Attribute Reductionmentioning

confidence: 99%

Neighborhood Attribute Reduction: A Multicriterion Strategy Based on Sample Selection

et al. 2018

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In the rough-set field, the objective of attribute reduction is to regulate the variations of measures by reducing redundant data attributes. However, most of the previous concepts of attribute reductions were designed by one and only one measure, which indicates that the obtained reduct may fail to meet the constraints given by other measures. In addition, the widely used heuristic algorithm for computing a reduct requires to scan all samples in data, and then time consumption may be too high to be accepted if the size of the data is too large. To alleviate these problems, a framework of attribute reduction based on multiple criteria with sample selection is proposed in this paper. Firstly, cluster centroids are derived from data, and then samples that are far away from the cluster centroids can be selected. This step completes the process of sample selection for reducing data size. Secondly, multiple criteria-based attribute reduction was designed, and the heuristic algorithm was used over the selected samples for computing reduct in terms of multiple criteria. Finally, the experimental results over 12 UCI datasets show that the reducts obtained by our framework not only satisfy the constraints given by multiple criteria, but also provide better classification performance and less time consumption.

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“…Pawlak [1,2] proposed rough sets theory in 1982 as a method of dealing with inaccuracy and uncertainty, and it has been developed into a variety of theories [3][4][5][6]. For example, the multi-granulation rough sets (MRS) model is one of the important developments [7,8]. The MRS can also be regarded as a mathematical framework to handle granular computing, which is proposed by Qian et al [9].…”

Section: Introductionmentioning

confidence: 99%

Novel Three-Way Decisions Models with Multi-Granulation Rough Intuitionistic Fuzzy Sets

Xue

Han

et al. 2018

Symmetry

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The existing construction methods of granularity importance degree only consider the direct influence of single granularity on decision-making; however, they ignore the joint impact from other granularities when carrying out granularity selection. In this regard, we have the following improvements. First of all, we define a more reasonable granularity importance degree calculating method among multiple granularities to deal with the above problem and give a granularity reduction algorithm based on this method. Besides, this paper combines the reduction sets of optimistic and pessimistic multi-granulation rough sets with intuitionistic fuzzy sets, respectively, and their related properties are shown synchronously. Based on this, to further reduce the redundant objects in each granularity of reduction sets, four novel kinds of three-way decisions models with multi-granulation rough intuitionistic fuzzy sets are developed. Moreover, a series of concrete examples can demonstrate that these joint models not only can remove the redundant objects inside each granularity of the reduction sets, but also can generate much suitable granularity selection results using the designed comprehensive score function and comprehensive accuracy function of granularities.

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Cost-sensitive rough set: A multi-granulation approach

Cited by 66 publications

References 59 publications

Pseudolabel Decision-Theoretic Rough Set

Pseudolabel Decision-Theoretic Rough Set

Neighborhood Attribute Reduction: A Multicriterion Strategy Based on Sample Selection

Novel Three-Way Decisions Models with Multi-Granulation Rough Intuitionistic Fuzzy Sets

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