Structured approximations as a basis for three-way decisions in rough set theory

Hu, Mengjun; Yao, Yiyu

doi:10.1016/j.knosys.2018.11.022

Cited by 72 publications

(19 citation statements)

References 32 publications

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“…Nowadays, rough set is still a research hotpot in the field of artificial intelligence. Hu and Yao proposed structured rough set approximation in complete and incomplete information systems to serve as a basis of three-way decisions with rough set [5]. To deal with an incomplete information system, a more generalized approach that considered potential candidates was presented [6].…”

Section: Introductionmentioning

confidence: 99%

Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection

Zhao

Zhu

2019

Applied Sciences

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With the rapid development of the network, data fusion becomes an important research hotspot. Large amounts of data need to be preprocessed in data fusion; in practice, the features of datasets can be filtered to reduce the amount of data. The feature selection based on fuzzy rough sets can process a large number of continuous and discrete data to reduce the data dimension, making the selected feature subset highly correlated with the classification but less dependent on other features. In this paper, a new method of fuzzy rough feature selection is proposed which combines the membership function determination method of fuzzy c-means clustering and fuzzy equivalence to the original selection. Different from the existing research, our method takes full advantage of knowledge about the dataset itself and the differences between datasets, which makes the features selected have a higher correlation with the classification, improves the classification accuracy, and reduces the data dimension. Experimental results on the UCI machine learning repository datasets confirmed the performance and effectiveness of our method. Compared to the existing method, smaller subsets of features and an average of 1% higher classification accuracies were achieved.

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Section: Introductionmentioning

confidence: 99%

Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection

Zhao

Zhu

2019

Applied Sciences

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“…(11) The proofs are similar to(8) and(9).Based on the Definition 10, the rough regions with regard tom i=1 A λ≤ i,k at information level ϕ are as follows: POS λ,ϕ…”

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confidence: 89%

“…Rough sets (RS) theory is an effective mathematical tool proposed by Pawlak in 1982 to address uncertainties and imprecisions [1]. It has been successfully applied in feature selection [2], safety monitoring data classification [3], decision making [4][5][6], information fusion [7], uncertainty analysis [8,9], medical diagnosis [10], and other fields [11][12][13][14]. Classical RS require strict inclusion relations between their equivalent classes and sets, and because there is no fault-tolerant mechanism, they are limited.…”

Section: Introductionmentioning

confidence: 99%

Double-Quantitative Generalized Multi-Granulation Set-Pair Dominance Rough Sets in Incomplete Ordered Information System

Xue

Zhang

et al. 2020

Symmetry

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Since the rough sets theory based on the double quantification method was proposed, it has attracted wide attention in decision-making. This paper studies the decision-making approach in Incomplete Ordered Information System (IOIS). Firstly, to better extract the effective information in IOIS, combined with the advantages of set-pair dominance relation and generalized multi-granulation, the generalized multi-granulation set-pair dominance variable precision rough sets (GM-SPD-VPRS) and the generalized multi-granulation set-pair dominance graded rough sets (GM-SPD-GRS) are proposed. Moreover, we discuss their related properties. Secondly, considering the GM-SPD-VPRS and the GM-SPD-GRS describe information from relative view and absolute view, respectively, we further combine the two rough sets to obtain six double-quantitative generalized multi-granulation set-pair dominance rough sets (GM-SPD-RS) models. Among them, the first two models fuse the approximation operators of two rough sets, and investigate the extreme cases of optimistic and pessimistic. The last four models combine the two rough sets by the logical disjunction operator and the logical conjunction operator. Then, we discuss relevant properties and derive the corresponding decision rules. According to the decision rules, an associated algorithm is constructed for one of the models to calculate the rough regions. Finally, we validate the effectiveness of these models with a medical example. The results indicate that the model is effective for dealing with practical problems.

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“…On the other hand, Three-way-In (TWI) learning originates from the seminal work on learning from ambiguous [19], superset [5] or partial labels [20] that proposed, under the standard optimization-based framework of modern Machine Learning literature, a generalization of the semi-supervised learning setting. Similar related approaches, which offer a different perspective that is more focused on attribute reduction and rule induction, have been investigated in the Rough Set and three-way decision communities by considering approaches applicable to semi-supervised and incomplete decision tables [21,22]. However, TWI learning consists in a generalization of these approaches by not assuming that the real label is in the superset labeling of a given instance x.…”

Section: Introduction and Related Workmentioning

confidence: 99%

The three-way-in and three-way-out framework to treat and exploit ambiguity in data

Campagner

Cabitza

Ciucci

2020

International Journal of Approximate Reasoning

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In this paper, we address ambiguity, intended as a characteristic of any data expression for which a unique meaning cannot be associated by the computational agent for either lack of information or multiple interpretations of the same configuration. In particular, we will propose and discuss ways in which a decision-support classifier can accept ambiguous data and make some (informative) value out of them for the decision maker. Towards this goal we propose a set of learning algorithms within what we call the three-way-in and three-way-out approach, that is, respectively, learning from partially labeled data and learning classifiers that can abstain. This approach is based on orthopartitions, as a common representation framework, and on three-way decisions and evidence theory, as tools to enable uncertain and approximate reasoning and inference. For both the above learning settings, we provide experimental results and comparisons with standard Machine Learning techniques, and show the advantages and promising performances of the proposed approaches on a collection of benchmarks, including a real-world medical dataset. a logic of incomplete information, cast in the setting of possibility theory.

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Structured approximations as a basis for three-way decisions in rough set theory

Cited by 72 publications

References 32 publications

Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection

Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection

Double-Quantitative Generalized Multi-Granulation Set-Pair Dominance Rough Sets in Incomplete Ordered Information System

The three-way-in and three-way-out framework to treat and exploit ambiguity in data

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