Summarizer: Fuzzy Rule-Based Classification Systems for Vertical and Horizontal Big Data

Tuy, Pétala Gardênia da Silva Estrela; Rios, Tatiane Nogueira

doi:10.1109/fuzz48607.2020.9177683

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Fuzzy Rule-Based Classification Systems (FRBCS's) [18] is a technique used to deal with classification problems [13] which has been applied to diverse problems, e.g., big data [32], image segmentation [21], health [22] and others. The Fuzzy Reasoning Method (FRM) [8,7] used by FRBCSs is a key component which is composed by four steps.…”

Section: Introductionmentioning

confidence: 99%

Application of the Sugeno Integral in Fuzzy Rule-Based Classification

Wieczynski

Lucca

Borges

et al. 2022

Intelligent Systems

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Fuzzy Rule-Based Classification System (FRBCS) is a well known technique to deal with classification problems. Recent studies have considered the usage of the Choquet integral and its generalizations to enhance the quality of such systems. Precisely, it was applied to the Fuzzy Reasoning Method (FRM) to aggregate the fired fuzzy rules when classify new data. On the other side, the Sugeno integral, another well known aggregation operator, obtained good results when applied to brain-computer interfaces. Those facts led to the present study in which we consider the Sugeno integral in classification problems. That is, the Sugeno integral is applied in the FRM of a widely used FRBCS and its performance is analyzed over 33 different datasets from the literature. In order to show the efficiency of this new approach, the obtained results are also compared to past studies involving the application of different aggregation functions. Finally, we perform a statistical analysis of the application.

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Section: Introductionmentioning

confidence: 99%

Application of the Sugeno Integral in Fuzzy Rule-Based Classification

Wieczynski

Lucca

Borges

et al. 2022

Intelligent Systems

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“…The last are considered in this paper and have been Supported by Navarra de Servicios y Tecnologías, S.A. (NASERTIC), CNPq (301618/2019-4, 305805/2021-5), FAPERGS (19/2551-0001660-3), the Spanish Ministry of Science and Technology (TIN2016-77356-P, PID2019-108392GB I00 (MCIN/AEI/10.13039/501100011033)). applied in different situations, such as, big data [6], image segmentation [7], health [8], anomaly detection [9] and many others.…”

Section: Introductionmentioning

confidence: 99%

Applying d-XChoquet integrals in classification problems

Wieczynski

Lucca

Borges

et al. 2022

2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Several generalizations of the Choquet integral have been applied in the Fuzzy Reasoning Method (FRM) of Fuzzy Rule-Based Classification Systems (FRBCS's) to improve its performance. Additionally, to achieve that goal, researchers have searched for new ways to provide more flexibility to those generalizations, by restricting the requirements of the functions being used in their constructions and relaxing the monotonicity of the integral. This is the case of CT-integrals, CC-integrals, CF-integrals, CF1F2-integrals and dCF-integrals, which obtained good performance in classification algorithms, more specifically, in the fuzzy association rule-based classification method for high-dimensional problems (FARC-HD). Thereafter, with the introduction of Choquet integrals based on restricted dissimilarity functions (RDFs) in place of the standard difference, a new generalization was made possible: the d-XChoquet (d-XC) integrals, which are ordered directional increasing functions and, depending on the adopted RDF, may also be a pre-aggregation function. Those integrals were applied in multi-criteria decision making problems and also in a motor-imagery brain computer interface framework. In the present paper, we introduce a new FRM based on the d-XC integral family, analyzing its performance by applying it to 33 different datasets from the literature.

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