Recent Advances in Decision Bireducts: Complexity, Heuristics and Streams

Stawicki, Sebastian; ŚlăźZak, Dominik

doi:10.1007/978-3-642-41299-8_19

Cited by 18 publications

(14 citation statements)

References 18 publications

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“…The harmony memory |H| is initialized randomly. For these subsets, corresponding fuzzyrough bireducts are produced and evaluated via Equations (22) and (23). Each selector has a note domain ℵ of |H| features, which may include identical or empty choices.…”

Section: B Heuristic Search For Fuzzy-rough Bireductsmentioning

confidence: 99%

“…Work such as [21] and [14] offer a solid basis for further extension of the fundamental concepts. Some initial attempts have also been made to quantify the optimality of any given bireduct using the idea of ε-bireducts [22], and heuristic search strategies [7]. This paper describes the further extension of the notion of fuzzy-rough bireducts as a general approach that can perform dimensionality reduction and data size reduction simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy-Rough Set Bireducts for Data Reduction

Parthaláin

Jensen

Diao³

2020

IEEE Trans. Fuzzy Syst.

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Section: B Heuristic Search For Fuzzy-rough Bireductsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy-Rough Set Bireducts for Data Reduction

Parthaláin

Jensen

Diao³

2020

IEEE Trans. Fuzzy Syst.

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“…Remark: Bireduct [36]- [37] is an attempt in rough sets to mix the process of reducing attributes and the process of selecting samples that are discernible by those attributes. In [38], it has been successfully applied to data stream, where data samples occur consecutively. It seems that the bireduct method resembles ASS-IAR since they both share the scheme of adding/removing samples and attributes.…”

Section: B Active Sample Selection Based Incremental Algorithm For Amentioning

confidence: 99%

Active Sample Selection Based Incremental Algorithm for Attribute Reduction With Rough Sets

Yang

Chen

Wang

2017

IEEE Trans. Fuzzy Syst.

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Attribute reduction with rough sets is an effective technique for obtaining a compact and informative attribute set from a given dataset. However, traditional algorithms have no explicit provision for handling dynamic datasets where data present themselves in successive samples. Incremental algorithms for attribute reduction with rough sets have been recently introduced to handle dynamic datasets with large samples, though they have high complexity in time and space. To address the time/space complexity issue of the algorithms, this paper presents a novel incremental algorithm for attribute reduction with rough sets based on the adoption of an active sample selection process and an insight into the attribute reduction process. This algorithm first decides whether each incoming sample is useful with respect to the current dataset by the active sample selection process. A useless sample is discarded while a useful sample is selected to update a reduct. At the arrival of a useful sample, the attribute reduction process is then employed to guide how to add and/or delete attributes in the current reduct. The two processes thus constitute the theoretical framework of our algorithm. The proposed algorithm is finally experimentally shown to be efficient in time and space.

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“…Therefore, reducts are focused on the reduction of the set of attributes. In order to reduce also the set of objects, the notion of bireduct arose [1][2][3]9,10]. In a general point of view, the main idea underlying bireducts is to choose the maximal consistent information subsystem.…”

Section: Introductionmentioning

confidence: 99%

Towards a Classification of Rough Set Bireducts

Benítez-Caballero

Medina

Ramírez‐Poussa

2020

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Size reduction mechanisms are very important in several mathematical fields. In rough set theory, bireducts arose to reduce simultaneously the set of attributes and the set of objects of the considered dataset, providing subsystems with the minimal sets of attributes that connect the maximum number of objects preserving the information of the original dataset. This paper presents the main properties of bireducts and how they can be used for removing inconsistencies.

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Recent Advances in Decision Bireducts: Complexity, Heuristics and Streams

Cited by 18 publications

References 18 publications

Fuzzy-Rough Set Bireducts for Data Reduction

Fuzzy-Rough Set Bireducts for Data Reduction

Active Sample Selection Based Incremental Algorithm for Attribute Reduction With Rough Sets

Towards a Classification of Rough Set Bireducts

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