Combining conflicting evidence based on Pearson correlation coefficient and weighted graph

Deng, Jixiang; Deng, Yong; Cheong, Kang Hao

doi:10.1002/int.22593

Cited by 76 publications

(27 citation statements)

References 41 publications

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“…In this paper, we used Pearson's correlation to determine the feature set that will be used for data analysis. Pearson's correlation examines the linear relationship between two variables [37]. Here, if the correlation value between two variables is close to −1, then these variables have a negative relationship.…”

Section: Feature Selectionmentioning

confidence: 99%

A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea

Park

Kim

2022

Applied Sciences

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In the airline industry, customer satisfaction occurs when passengers’ expectations are met through the airline experience. Considering that airline service quality is the main factor in obtaining new and retaining existing customers, airline companies are applying various approaches to improve the quality of the physical and social servicescapes. It is common to use data analysis techniques for analyzing customer propensity in marketing. However, their application to the airline industry has traditionally focused solely on surveys; hence, there is a lack of attention paid to deep learning techniques based on survey results. This study has two purposes. The first purpose is to find the relationship between various factors influencing customer churn risk and satisfaction by analyzing the airline customer data. For this, we applied deep learning techniques to the survey data collected from the users who have used mostly Korean airplanes. To the best of our knowledge, this is the one of the few attempts at applying deep learning to analyze airline customer propensities. The second purpose is to analyze the influence of the social servicescape, including the viewpoints of the cabin crew and passengers using aircraft, on airline customer propensities. The experimental results demonstrated that the proposed method of considering human services increased the accuracy of predictive models by up to 10% and 9% in predicting customer churn risk and satisfaction, respectively.

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Section: Feature Selectionmentioning

confidence: 99%

A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea

Park

Kim

2022

Applied Sciences

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“…H1: Proper understanding CSR by companies and its engagement has a positive impact on employees' satisfaction. The next step of research is the calculation of the chi-square test, which allows the determination of Pearson's C coefficient (Deng et al, 2021). The Chi square value allows you to calculate the C-Pearson coefficient, which takes values from 0 to 1.…”

Section: Name Of Csr Initiativementioning

confidence: 99%

Employee perception of CSR and its effects on the company’s image

Rosak-Szyrocka

Żywiołek

Shengelia

et al. 2022

Production Engineering Archives

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Understanding and promoting the CSR is an important societal issue, and circumstances force modern-minded management to abandon the classical model of only the necessary fulfilment of legally set obligations. There has been a lot of study done on employee perceptions of CSR in the workplace, but very little has been done on how employees comprehend CSR and how they received it at their work-place in chosen countries. Research was carried out based on a questionnaire survey with data collection from September 2020 to March 2021, with a total of 1103 respondents. The aim of the article is to describe the CSR concept among employees meaning in selected countries: Zimbwabwe, Czech Republic, Poland. The measuring instrument used in the study was the questionnaire method, in which the research tool is a questionnaire CAWI - Computer Assisted Web Interview. It was shown that enterprises apply CSR activities, but they are not fully aware of the CSR benefits.

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“…The most essential step in information fusion is the weight assignment of sources [13]. Numerous excellent approaches, such as granular computing [15], soft sets [12], fuzzy theory [19,29], complex networks [14,25,57], machine learning [17], evidence theory [7,46], and others [18,34,35], have been applied to handle uncertain information. In the field of information combination, Dempster-Shafer evidence theory (D-S) is essential [6,36] as a fundamental method to handle the uncertainty in information systems.…”

Section: Introductionmentioning

confidence: 99%

Combining time-series evidence: A complex network model based on a visibility graph and belief entropy

Song

Xiao

2022

Appl Intell

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Combining basic probability assignments (BPAs) with time series is common in real-life cases. Therefore, a new evidence fusion approach based on belief entropy and a visibility graph (BE-VG) is proposed. The approach converts a time-series BPA into a weighted visibility graph (WVG). In addition, some numerical examples are illustrated to illustrate the efficiency and applicability of the proposed method. Finally, to demonstrate the effect of the BE-VG method, the proposed method is applied to electroencephalogram (EEG) dynamic fusion. Experimentally, the results indicate that the BE-VG method is effective and accurate in conducting EEG signal fusion.

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Combining conflicting evidence based on Pearson correlation coefficient and weighted graph

Cited by 76 publications

References 41 publications

A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea

A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea

Employee perception of CSR and its effects on the company’s image

Combining time-series evidence: A complex network model based on a visibility graph and belief entropy

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