Cost-sensitive rough set approach

Ju, Hengrong; Yang, Xibei; Yu, Hualong; Li, Tong-Jun; Yu, Dong‐Jun; Yang, Jingyu

doi:10.1016/j.ins.2016.01.103

Cited by 46 publications

(10 citation statements)

References 41 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%

“…Consequently, DTRS may contain two important aspects: (1) information granulation and (2) decision costs. On the one hand, different types of information granulations may be used to construct different models [4,[13][14][15][16][17][18][19]. For example, the classical DTRS is formed based on the indiscernibility relation [4]; similar to Pawlak's rough set, such model is only useful in analyzing categorical data; Li et al [16] have proposed a neighborhood based DTRS, in which the result of information granulation is expressed by neighborhoods of samples; Song et al [15] have proposed a fuzzy based DTRS, in which the result of information granulation is reflected by fuzzy relation.…”

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

“…Rough set theory [1] is an effective mathematical tool to qualitatively and quantitatively describe the uncertainty information in data. Due to such characteristics, it has been frequently applied in attribute reduction [2][3][4][5][6][7][8][9], which aims to select a condition attribute subset that can retain the identifiable ability of the original data. It should be pointed out that among existing attribute reduction methods, fuzzy rough set-based ones [10][11][12][13][14][15] are widely concerned with handling indiscernibility and fuzziness in data with realvalued condition attributes.…”

Section: Introductionmentioning

confidence: 99%

Adjustable Fuzzy Rough Reduction: A Nested Strategy

She

et al. 2021

Computational Intelligence and Neuroscience

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As a crucial extension of Pawlak's rough set, a fuzzy rough set has been successfully applied in real-valued attribute reduction. Nevertheless, the traditional fuzzy rough set is not provided with adjustable ability due to the maximal and minimal operators. It follows that the associated measure for attribute evaluation is not always appropriate. To alleviate such problems, a novel adjustable fuzzy rough set model is presented and further introduced into the parameterized attribute reduction. Additionally, the inner relationship between the appointed parameter and the reduct result is discovered, and thereby a nested mechanism is adopted to accelerate the searching procedure of reduct. Experiments demonstrate that the proposed heuristic algorithm can offer us more stable reducts with higher computational efficiency as compared with the traditional approaches.

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“…Firstly, binary relation is effective to conduct information granulation [17][18][19][20][21]. In the classical rough set [22][23][24][25], binary relation is an equivalence relation, and each single equivalence class can be regarded as an information granule [26].…”

Section: Introductionmentioning

confidence: 99%

Combined Accelerator for Attribute Reduction: A Sample Perspective

Chen

Jin

Liu

et al. 2020

Mathematical Problems in Engineering

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In the field of neighborhood rough set, attribute reduction is considered as a key topic. Neighborhood relation and rough approximation play crucial roles in the process of obtaining the reduct. Presently, many strategies have been proposed to accelerate such process from the viewpoint of samples. However, these methods speed up the process of obtaining the reduct only from binary relation or rough approximation, and then the obtained results in time consumption may not be fully improved. To fill such a gap, a combined acceleration strategy based on compressing the scanning space of both neighborhood and lower approximation is proposed, which aims to further reduce the time consumption of obtaining the reduct. In addition, 15 UCI data sets have been selected, and the experimental results show us the following: (1) our proposed approach significantly reduces the elapsed time of obtaining the reduct; (2) compared with previous approaches, our combined acceleration strategy will not change the result of the reduct. This research suggests a new trend of attribute reduction using the multiple views.

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Cost-sensitive rough set approach

Cited by 46 publications

References 41 publications

Pseudolabel Decision-Theoretic Rough Set

Pseudolabel Decision-Theoretic Rough Set

Adjustable Fuzzy Rough Reduction: A Nested Strategy

Combined Accelerator for Attribute Reduction: A Sample Perspective

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