Granular cabin: An efficient solution to neighborhood learning in big data

Liu, Keyu; Li, Tianrui; Yang, Xibei; Yang, Xin; Liu, Dun; Zhang, Pengfei; Wang, Jie

doi:10.1016/j.ins.2021.11.034

Cited by 37 publications

(5 citation statements)

References 44 publications

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“…Four algorithms based on the Rough Set theory and its variants and extensions were tested. The analysis and comparison of results allowed us to understand the superiority of the approaches based on Fuzzy Rough Sets for the interpretation of the phenomenon even if further experiments are needed, including, for example, those on big data which may require more efficient approaches such as the one reported in [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

A novel approach based on rough set theory for analyzing information disorder

et al. 2022

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The paper presents and evaluates an approach based on Rough Set Theory, and some variants and extensions of this theory, to analyze phenomena related to Information Disorder. The main concepts and constructs of Rough Set Theory, such as lower and upper approximations of a target set, indiscernibility and neighborhood binary relations, are used to model and reason on groups of social media users and sets of information that circulate in the social media. Information theoretic measures, such as roughness and entropy, are used to evaluate two concepts, Complexity and Milestone, that have been borrowed by system theory and contextualized for Information Disorder. The novelty of the results presented in this paper relates to the adoption of Rough Set Theory constructs and operators in this new and unexplored field of investigation and, specifically, to model key elements of Information Disorder, such as the message and the interpreters, and reason on the evolutionary dynamics of these elements. The added value of using these measures is an increase in the ability to interpret the effects of Information Disorder, due to the circulation of news, as the ratio between the cardinality of lower and upper approximations of a Rough Set, cardinality variations of parts, increase in their fragmentation or cohesion. Such improved interpretative ability can be beneficial to social media analysts and providers. Four algorithms based on Rough Set Theory and some variants or extensions are used to evaluate the results in a case study built with real data used to contrast disinformation for COVID-19. The achieved results allow to understand the superiority of the approaches based on Fuzzy Rough Sets for the interpretation of our phenomenon.

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Section: Discussionmentioning

confidence: 99%

A novel approach based on rough set theory for analyzing information disorder

et al. 2022

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“…So, we can see that SNFJE or SNFDI always achieves the best classification performance. Specifically, in Figure 2, for CART classification accuracies, SNFJE and SNFDI attain the maximal accuracies in 8/12 data sets (i.e., Data set 1, 2, 4, 5, 6, 8, 9, 12); for KNN classification accuracies, SNFJE and SNFDI attain the maximal accuracies in 8/12 data sets (i.e., Data set 1, 2, 4, 6, 7, 8, 9, 12); for SVM classification accuracies, SNFJE and SNFDI attain the maximal accuracies in 8/12 data sets (i.e., Data set 2, 3,4,7,8,9,12). It should be noticed that, although our proposed methods are sometimes defeated by ALL (i.e., all the original features), it is still evidently superiority to other attribute reduction algorithms.…”

Section: B Comparisons On Classificationmentioning

confidence: 99%

“…I N 1982, Professor Z. Pawlak coined the rough set theory which is generally acknowledged as an efficient and relatively new mathematical implement to process the incomplete, inaccurate, and undefinable data [1]- [3]. In rough set theory, no extra information is needed, thus it has gained huge attention from considerable number of researchers across a lot of fields, to name a few, artificial intelligence, decision making, machine learning, granular computing, data mining [4]- [6].…”

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confidence: 99%

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Gao

2024

IEEE Access

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As a very prominent research application of the theory of rough sets, attribute reduction technique has made significant strides in a lot of fields, including decision making, granular computing, etc. In particular, fuzzy attribute reduction approaches contribute greatly in the presence of uncertain data. However, most of fuzzy relations used in these approaches lack the discriminant ability to sample similarity, failing to identify the feature significance satisfactorily. In this article, a novel scheme using the shared neighborhood fuzzy uncertainties is proposed. Firstly, the concept of shared neighborhood is formulated, and then employed to establish the fuzzy similarity relation that effectively captures the sample similarity. Secondly, two fuzzy uncertainty measures named joint entropy and discrimination index based on shared neighborhood fuzzy relation are defined, which can quantify the feature's significance to the uncertainty characterization. Finally, two heuristic searching algorithms are designed to identify reducts aimed at minimizing the fuzzy uncertainties. Some comparative studies are investigated to examine the advantage of the designed reduction algorithms in classifier modeling. The reported analyses on public data sets verify that the designed algorithms outperform some representative and latest algorithms.

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“…The core of such a phase is to discriminate those features with inferior quality and remove them from the raw features. The specific process of one popular backward searching named backward greedy searching (BGS) [35,36] is as follows: (1) given a predefined constraint, each object in raw feature is evaluated by a measure, and those unqualified features are selected; (2) selected features from raw features are removed; (3) if the constraint is satisfied by the remaining features, the search process is terminated. • Random searching.…”

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confidence: 99%

Parallel Selector for Feature Reduction

et al. 2023

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In the field of rough set, feature reduction is a hot topic. Up to now, to better guide the explorations of this topic, various devices regarding feature reduction have been developed. Nevertheless, some challenges regarding these devices should not be ignored: (1) the viewpoint provided by a fixed measure is underabundant; (2) the final reduct based on single constraint is sometimes powerless to data perturbation; (3) the efficiency in deriving the final reduct is inferior. In this study, to improve the effectiveness and efficiency of feature reduction algorithms, a novel framework named parallel selector for feature reduction is reported. Firstly, the granularity of raw features is quantitatively characterized. Secondly, based on these granularity values, the raw features are sorted. Thirdly, the reordered features are evaluated again. Finally, following these two evaluations, the reordered features are divided into groups, and the features satisfying given constraints are parallel selected. Our framework can not only guide a relatively stable feature sequencing if data perturbation occurs but can also reduce time consumption for feature reduction. The experimental results over 25 UCI data sets with four different ratios of noisy labels demonstrated the superiority of our framework through a comparison with eight state-of-the-art algorithms.

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Granular cabin: An efficient solution to neighborhood learning in big data

Cited by 37 publications

References 44 publications

A novel approach based on rough set theory for analyzing information disorder

A novel approach based on rough set theory for analyzing information disorder

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Parallel Selector for Feature Reduction

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