Interactive and Complementary Feature Selection via Fuzzy Multigranularity Uncertainty Measures

Wan, Jihong; Li, Tianrui; Yuan, Zhong; Liu, Jia; Huang, Wei

doi:10.1109/tcyb.2021.3112203

Cited by 49 publications

(5 citation statements)

References 44 publications

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“…One of the examples is the fuzzy multi neighborhood rough set model ( Wan et al, 2021a ), which effectively improves the performance of the classification of subsets while at the same time reducing the performance of spatial features. In addition, Wan et al (2021b) have developed another approach based on the fuzzy rough set, which is the dynamic interactions method. In future research, different fuzzy rough approaches should be used to find out the advantages and disadvantages of these approaches.…”

Section: Discussionmentioning

confidence: 99%

Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function

Pamučar

Puška²,

Šimić

et al. 2023

Engineering Applications of Artificial Intelligence

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Section: Discussionmentioning

confidence: 99%

Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function

Pamučar

Puška²,

Šimić

et al. 2023

Engineering Applications of Artificial Intelligence

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“…QOBL is an improved version of opposition-based learning (OBL) [ 56 ], which considers the individual with the opposite position to the current individual may be closer than the current individual. In recent years, the QOBL strategy has been used in MAs [ 57 – 60 ] to improve convergence speed accuracy.…”

Section: The Proposed Qgbwoamentioning

confidence: 99%

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

et al. 2022

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Whale optimization algorithm (WOA) tends to fall into the local optimum and fails to converge quickly in solving complex problems. To address the shortcomings, an improved WOA (QGBWOA) is proposed in this work. First, quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions. Second, a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA. To verify the advantages of QGBWOA, comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10, 30, 50, and 100 and on CEC 2020 test with dimension 30. Furthermore, the performance results were tested using Wilcoxon signed-rank (WS), Friedman test, and post hoc statistical tests for statistical analysis. Convergence accuracy and speed are remarkably improved, as shown by experimental results. Finally, feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems. QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics. Supplementary Information The online version contains supplementary material available at 10.1007/s42235-022-00297-8.

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“…In this context, the selection of effective subsets of features is crucial for the efficacy of the proposed approached since the analysis of information disorder relies on the correct classification of content (e.g., real, fake) and users (i.e., interpreters) behaviors, and these are tasks that benefit from good attributes reduction and features selection algorithms. Approaches such as [29] and [48] are especially useful since rely on the Granular Computing paradigm for the tasks of attributes reduction and feature selection. These approaches based on Granular Computing have the advantage of considering different levels of granularity (multi-granularity) in the attribute reduction and feature selection for improving classification performances.…”

Section: Related Workmentioning

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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Interactive and Complementary Feature Selection via Fuzzy Multigranularity Uncertainty Measures

Cited by 49 publications

References 44 publications

Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function

Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel–Alsina Function

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

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

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