The Impact on Life Satisfaction of Nursing Students Using the Fuzzy Regression Model

Yoon, Hee Sang; Choi, Seung Hoe

doi:10.5391/ijfis.2019.19.2.59

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Without a loss of generality, all fuzzy parameters and variables in the proposed methodology are given as or approximated with triangular fuzzy numbers (TFNs). We also assumed that all experts apply the following FLR to forecast the same target y based on decision variable values {x i } [19,30]:…”

Section: Preliminary Models For Fitting a Fuzzy Linear Regressionmentioning

confidence: 99%

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

2020

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Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error.

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Section: Preliminary Models For Fitting a Fuzzy Linear Regressionmentioning

confidence: 99%

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

2020

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“…In this study, [29] methodology is leveraged to incorporate ranking fuzzy numbers with integral value in order to convert fuzzy numbers to their associated linguistic term.…”

Section: Computational Proceduresmentioning

confidence: 99%

Towards Fuzzy Analytics for Digital Video Advertising Campaign Effectiveness and Customer Experience

Dairo¹,

Szűcs²

2019

IJFIS

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The recent growth of digital marketing channels has continued to drive the increase in brands' budgetary allocation for these marketing channels. However, questions are beginning to arise from marketing practitioners and brands on the justification of these budgets along these digital channels concerning their effectiveness and customer experience. This paper focuses on the aspect of mobile digital video advertisement with a view of examining its effectiveness and how viewers experience can be measured. This article proposes a fuzzy measurement approach to mobile video ad campaign experience using triangular fuzzy number evaluation approach. The experiment includes a comprehensive study on three different groups of mobile video ad viewers on a mobile network. A broad characteristic cross-section of mobile users with different types of mobile devices was randomly selected (N = 150) for empirical analysis. Videos and ads were tracked for these segmented users for 14 days. Also, a real-time online survey was enabled for these users and captured. This study models and clarifies fuzzy linguistic approach for measuring customer experience of mobile video ad campaign viewers. The result gives a better overall understanding of customer experience to advertisers and brands rather than relying only on score or scale of measurement.

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“…Local learning techniques, such as k-nearest neighbors (k-NN) classifiers [6], linear regression interpolation, and local interpolation [7], have been successfully applied to various learning tasks (refer to [8][9][10][11][12][13]).…”

Section: Introductionmentioning

confidence: 99%

Lazy Learning for Nonparametric Locally Weighted Regression

Roh¹,

Kim²,

Ahn³

2020

IJFIS

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In this study, a newly designed local model called locally weighted regression model is proposed for the regression problem. This model predicts the output for a newly submitted data point. In general, the local regression model focuses on an area of the input space specified by a certain kernel function (Gaussian function, in particular). The local area is defined as a region enclosed by a neighborhood of the given query point. The weights assigned to the local area are determined by the related entries of the partition matrix originating from the fuzzy C-means method. The local regression model related to the local area is constructed using a weighted estimation technique. The model exploits the concept of the nearest neighbor, and constructs the weighted least square estimation once a new query is provided given. We validate the modeling ability of the overall model based on several numeric experiments.

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The Impact on Life Satisfaction of Nursing Students Using the Fuzzy Regression Model

Cited by 4 publications

References 20 publications

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

Towards Fuzzy Analytics for Digital Video Advertising Campaign Effectiveness and Customer Experience

Lazy Learning for Nonparametric Locally Weighted Regression

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