An overview of recent distributed algorithms for learning fuzzy models in Big Data classification

Ducange, Pietro; Fazzolari, Michela; Marcelloni, Francesco

doi:10.1186/s40537-020-00298-6

Cited by 18 publications

(7 citation statements)

References 70 publications

(115 reference statements)

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“…In the data preparation phase, it first summarizes the characteristics of the data. en, the research results are deduced by using the built mining model, which finally analyzes the deduction results and tests the validity of data mining [17]. economy.…”

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confidence: 99%

The Impact of Tax Reduction and Fee Reduction Based on Big Data Algorithm on the High-Quality Development of the Real Economy under the Action of Coupling Effect or Substitution Effect

Zhang

2022

Computational Intelligence and Neuroscience

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The basic idea of the mass of medical growth is to adhere to local market price thinking with a Chinese touch and follow the development policy of “quality first, efficiency first.” It insists on properly handling a series of important relationships betwixt socialism and market economy, the first to drive the rich later, the government and the market, equality and efficiency, short-term growth and long-term development, China and the international economy, ecology and growth of the region. Under the guidance of the qualitative thinking theory, it combines the strategic goals of China’s economic qualitative development and actively draws on the research results of other countries. It uses big data algorithms to focus on the impact of qualitative development on tax and income reduction in the real economy. It conducts research experiments on the impact of tax reduction and fee reduction based on big data algorithms on the top-notch growth of the real economy. Its experimental data show that: in 2018, the share of primary, tertiary, and primary sector in China’s dimensional economy top-notch growth coordination index was 7.2%, 40.7%, and 52.2%, respectively. Its contribution rate to economic growth was 4.2%, 36.1%, and 59.7%, respectively. From these data, it can be concluded that the top-notch growth of the real economy is getting better and better under the influence of tax reduction and fee reduction by big data algorithms.

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confidence: 99%

The Impact of Tax Reduction and Fee Reduction Based on Big Data Algorithm on the High-Quality Development of the Real Economy under the Action of Coupling Effect or Substitution Effect

Zhang

2022

Computational Intelligence and Neuroscience

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“…The FES created uses fuzzy logic due to its ability to deal with qualitative and subjective human thoughts, such as inaccurate thinking, because a lack of clarity is impossible to use in classical logic. Ducange, Fazzolari, and Marcelloni (2020) confirm the ability of fuzzy logic to deal with vague and inaccurate data and its innate characteristics as interpretable.…”

Section: Fuzzy Expert System (Fes) As a Decision Support System (Dss)mentioning

confidence: 82%

“…In fact, the application of fuzzy logic has been improving in different areas since 2014 (Ducange et al, 2020); we can mention some current research that deals with its use with the sustainability of renewable energies. Stevovic et al (2015) offered a model for incorporation of environmental and local community parameters for the decision-making process of hydro plants using fuzzy logic.…”

Section: Fuzzy Expert System (Fes) As a Decision Support System (Dss)mentioning

confidence: 99%

System for assessing the sustainability conditions of small hydro plants by fuzzy logic

Clauberg

Mello

Simioni

et al. 2020

Sustainable Development

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This work presents a system for an integrated evaluation of the implementation of small hydroelectric power plants (SHPs), based on a soft decision tree. The method generates the enterprise's sustainability index, where economic, environmental, and social aspects are considered in its interactions, with indicators measured in the field, sectoral indexes, and final index generated by fuzzy logic rule blocks. The methodology used to select the indicators and indexes followed four phases. First, a fuzzy expert system (FES) was developed in the form of a soft decision tree. Then, the array of suggested variables and weights were submitted to expert analysis using the Delphi methodology, and their assessments were collected. The third phase was the improvement of the system, taking into account the obtained considerations.In the fourth phase, the system was applied to a case study based on the installation of an SHP in the southern region of Brazil. The results obtained in the case study showed the sustainability of the enterprise. The presented method can have a significant contribution in supporting decisions regarding the licensing of hydroelectric projects.

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“…Multiobjective evolutionary algorithms (MOEAs) have been successfully employed to tackle this optimization problem and the term multi-objective evolutionary fuzzy systems (MOEFSs) has been coined [12,28] to identify FRBSs generated by MOEAs. Since then, many papers have proposed the use of MOEFSs in classification problems [16,[29][30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Explainable Internet Traffic Classification

et al. 2021

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The problem analyzed in this paper deals with the classification of Internet traffic. During the last years, this problem has experienced a new hype, as classification of Internet traffic has become essential to perform advanced network management. As a result, many different methods based on classical Machine Learning and Deep Learning have been proposed. Despite the success achieved by these techniques, existing methods are lacking because they provide a classification output that does not help practitioners with any information regarding the criteria that have been taken to the given classification or what information in the input data makes them arrive at their decisions. To overcome these limitations, in this paper we focus on an “explainable” method for traffic classification able to provide the practitioners with information about the classification output. More specifically, our proposed solution is based on a multi-objective evolutionary fuzzy classifier (MOEFC), which offers a good trade-off between accuracy and explainability of the generated classification models. The experimental results, obtained over two well-known publicly available data sets, namely, UniBS and UPC, demonstrate the effectiveness of our method.

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An overview of recent distributed algorithms for learning fuzzy models in Big Data classification

Cited by 18 publications

References 70 publications

The Impact of Tax Reduction and Fee Reduction Based on Big Data Algorithm on the High-Quality Development of the Real Economy under the Action of Coupling Effect or Substitution Effect

The Impact of Tax Reduction and Fee Reduction Based on Big Data Algorithm on the High-Quality Development of the Real Economy under the Action of Coupling Effect or Substitution Effect

System for assessing the sustainability conditions of small hydro plants by fuzzy logic

Explainable Internet Traffic Classification

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