Multi-granularity Attribute Reduction

Liang, Shaochen; Liu, Keyu; Chen, Xiangjian; Wang, Pingxin; Yang, Xibei

doi:10.1007/978-3-319-99368-3_5

Cited by 1 publication

(1 citation statement)

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“…Neighborhood rough set is another generalization of classical rough set. There are two main advantages for neighborhood rough set: (1) it is suitable for dealing with continuous data or even mixed data, mainly because the neighborhood relation is constructed based on the consideration of distance between samples; (2) the scale of neighborhood provides us with a flexible technique to measure the granularity [38] and then the structure of multigranularity [39,40] can be naturally formed.…”

Section: Preliminary Knowledgementioning

confidence: 99%

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: Preliminary Knowledgementioning

confidence: 99%