Attribute Reduction: An Ensemble Strategy

Xu, Suping; Wang, Pingxin; Li, Jinhai; Yang, Xibei; Chen, Xiangjian

doi:10.1007/978-3-319-60837-2_30

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…Up to now, through considering various constraints, a lot of the researchers have proposed so many different definitions of attribute reductions [43][44][45][46][47][48]. It should be noticed that, for DTRS related attribute reductions, decision costs provide us with an interesting topic [11,49,50].…”

Section: Attribute Reduction In Plndtrsmentioning

confidence: 99%

“…Algorithm to Compute Reduct. Presently, in the field of rough set, it is well known that obtaining all reducts is an NP-hard problem [51,52]; it follows that many heuristic algorithms for finding reducts have been studied [45,53]. Generally speaking, a heuristic algorithm contains two core aspects: heuristic information and searching strategy.…”

Section: Definition Given a Pseudolabel Decision System ∀ ⊆mentioning

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: Attribute Reduction In Plndtrsmentioning

confidence: 99%

Section: Definition Given a Pseudolabel Decision System ∀ ⊆mentioning

confidence: 99%

Pseudolabel Decision-Theoretic Rough Set

Wang

Zhang

Liu

et al. 2019

Mathematical Problems in Engineering

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“…As a whole, the key step of Algorithm 1 is to select the most significant attribute. Nevertheless, the changing of data is inevitable in real world applications [21], e.g., the condition attribute values of some samples are updated. Then, the most significant attribute may no longer be the one obtained before the variation of data, and it follows that a completely different reduct occurs.…”

Section: Ensemble Heuristic Algorithmmentioning

confidence: 99%

Stable attribute reduction for neighborhood rough set

Liang¹,

Yang²,

Chen³

et al. 2018

Filomat

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In neighborhood rough set theory, traditional heuristic algorithm for computing reducts does not take the stability of the selected attributes into account, it follows that the performances of the reducts may not be good enough if the perturbations of data occur. To fill the gap, the mechanism of acquiring the most significant attribute is realized by two steps in the reduction process: firstly, several important attributes are derived in each iteration based on several radii which are close to the given radius for computing reduct; secondly, the most significant attribute is selected from them by a voting strategy. The experiments verify that such method can effectively improve the stabilities of the reducts, and it does not require too much attributes for constructing the reducts.

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