Rough sets in distributed decision information systems

Hu, Jun; Pedrycz, Witold; Wang, Guoyin; Wang, Kai

doi:10.1016/j.knosys.2015.10.025

Cited by 36 publications

(6 citation statements)

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“…Within another context, the context of limited labeled big data, in [32], authors introduced a theoretic framework called local rough set and developed a series of corresponding concept approximation and attribute reduction algorithms with linear time complexity, which can efficiently and effectively work in limited labeled big data. In the context of distributed decision information systems, i.e., several separate data sets dealing with different contents/topics but concerning the same data items, in [19], authors proposed a distributed definition of rough sets to deal with the reduction of these information systems.…”

Section: Literature Reviewmentioning

confidence: 99%

A scalable and effective rough set theory-based approach for big data pre-processing

et al. 2020

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A big challenge in the knowledge discovery process is to perform data pre-processing, specifically feature selection, on a large amount of data and high dimensional attribute set. A variety of techniques have been proposed in the literature to deal with this challenge with different degrees of success as most of these techniques need further information about the given input data for thresholding, need to specify noise levels or use some feature ranking procedures. To overcome these limitations, rough set theory (RST) can be used to discover the dependency within the data and reduce the number of attributes enclosed in an input data set while using the data alone and requiring no supplementary information. However, when it comes to massive data sets, RST reaches its limits as it is highly computationally expensive. In this paper, we propose a scalable and effective rough set theory-based approach for large-scale data pre-processing, specifically for feature selection, under the Spark framework. In our detailed experiments, data sets with up to 10,000 attributes have been considered, revealing that our proposed solution achieves a good speedup and performs its feature selection task well without sacrificing performance. Thus, making it relevant to big data. Keywords Big data • Data pre-processing • Rough set theory • Distributed processing • Scalability • High-performance computing This work is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 702527.

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Section: Literature Reviewmentioning

confidence: 99%

A scalable and effective rough set theory-based approach for big data pre-processing

et al. 2020

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“…The rough set theory is widely known as a reasonable and efficient soft computing method for handling several decision making situations via attribute selections and rule acquisitions; see [19,27,28]. Moreover, in the past decades, various generalized rough set models have been constructed in step with the actual demands of real-world situations; see [26,30].…”

Section: Introductionmentioning

confidence: 99%

On category of T-rough sets

Khodaii

Estaji

Anvariyeh

2022

Filomat

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We introduce three new categories in which their objects are T-approximation spaces and they are denoted by NTAprS, RNTAprS, and LNTAprS. We verify the existence or nonexistence of products and coproducts in these three categories and characterized theirs epimorphisms and monomorphisms. We discuss equalizer and coequalizer of a pair of morphisms in the three categories. We introduce the notion of idempotent approximation space, and we show that idempotent approximation spaces and right upper natural transformations form a category, which is denoted by RNTApr2S. Let CS be the category of all closure spaces and closure preserving mappings. We define a functor F from RNTApr2S to CS and show that F is a full functor and every object of CS has a corefiection along F.

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“…The first one is to process big data in an allowable time [16] [27]. The second one is to omit the redundant and unnecessary features within the data that may impede classification ability and increase the consumption of computation and memory storage [1] [18]. Running parallel algorithms on a cluster is one common way to handle a large amount of data when it cannot be processed on a single computer.…”

Section: Introductionmentioning

confidence: 99%

Parallel attribute reduction in dominance-based neighborhood rough set

Chen

Cai

et al. 2016

Information Sciences

142

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The amount of data collected from different real-world applications is increasing rapidly. When the volume of data is too large to be loaded to memory, it may be impossible to analyze it using a single computer. Although efforts have been taken to manage big data by using a single computer, the problem may not be solved in an acceptable time frame, making parallel computing an indispensable way to handle big data. In this paper, we investigate approaches to attribute reduction in parallel using dominance-based neighborhood rough sets (DNRS), which take into consideration the partial orders among numerical and categorical attribute values, and can be utilized in a multicriteria decision-making method. We first present some properties of attribute reduction in DNRS, and then investigate principles of parallel attribute reduction in DNRS. Parallelization on different components of attribute reduction are explored in detail. Furthermore, parallel attribute reduction algorithms in DNRS are proposed. Experimental results on UCI data and big data show that the proposed parallel algorithm is both effective and efficient.

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Rough sets in distributed decision information systems

Cited by 36 publications

References 50 publications

A scalable and effective rough set theory-based approach for big data pre-processing

A scalable and effective rough set theory-based approach for big data pre-processing

On category of T-rough sets

Parallel attribute reduction in dominance-based neighborhood rough set

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