Multi-granular Evaluation Model Through Fuzzy Random Regression to Improve Information Granularity

2014 ◽  
pp. 231-245
Author(s):  
Nureize Arbaiy ◽  
Junzo Watada
Keyword(s):  
Evaluation Model ◽  
Random Regression ◽  
Information Granularity ◽  
Fuzzy Random

Building Confidence-Interval-Based Fuzzy Random Regression Models

2009 ◽  
Vol 17 (6) ◽  
pp. 1273-1283 ◽  
Author(s):  
J. Watada ◽  
Shuming Wang ◽  
W. Pedrycz
Keyword(s):  
Confidence Interval ◽  
Regression Models ◽  
Random Regression ◽  
Fuzzy Random

scholarly journals Bias in genetic evaluation using random regression test-day model

2008 ◽  
Vol 16 (2) ◽  
pp. 103 ◽  
Author(s):  
M. LIDAUER ◽  
E.A. MÄNTYSAARI
Keyword(s):  
Animal Model ◽  
Evaluation Model ◽  
Protein Yield ◽  
Random Regression ◽  
Data Sets ◽  
Heterogeneous Variance ◽  
Breeding Values ◽  
Regression Test ◽  
Estimated Breeding Values ◽  
Reduced Data

The effect of an upgraded Finnish evaluation model on bias in estimated breeding values for protein yield was investigated. Evaluations based on repeatability animal model and on random regression test-day model without and with heterogeneous variance adjustment were compared. Comparisons were based on the average difference between pedigree indices and the future estimated breeding values, based on own or on daughter performance records. This was defined as empirical bias. The pedigree indices were computed from reduced data sets where four years of the most recent data were excluded. Results showed an upward bias in the protein yield pedigree indices for Ayrshire young sires of 2.2 kg, 2.5 kg and 1.8 kg from the repeatability animal model, random regression test-day model and random regression test-day model with heterogeneous variance adjustment, respectively. Pedigree indices for daughters of young sires were upward biased, whereas pedigree indices for daughters of proven sires were slightly underestimated when heterogeneous variance was not accounted. Inclusion of test-day yields from the fourth lactation onwards increased the bias. Moving from repeatability animal model to random regression test-day model did not reduce the bias, whereas adjustment of heterogeneous variance reduced bias.;

Confidence interval calculator for fuzzy random regression

2018 ◽  
Author(s):  
Ng Pui Xiang ◽  
Nureize Arbaiy ◽  
Hamijah Ab Rahman ◽  
Mohd Zaki Mohd Salikon ◽  
Pei-Chun Lin
Keyword(s):  
Confidence Interval ◽  
Random Regression ◽  
Fuzzy Random

Fuzzy Random Regression-Based Modeling in Uncertain Environment

2016 ◽  
pp. 127-146 ◽  
Author(s):  
Nureize Arbaiy ◽  
Junzo Watada ◽  
Pei-Chun Lin
Keyword(s):  
Mathematical Model ◽  
Statistical Data ◽  
Real Life ◽  
Random Regression ◽  
Percentage Error ◽  
Practical Case ◽  
Fuzzy Decision ◽  
Planning Models ◽  
Value Determination ◽  
Fuzzy Random

The parameter value determination is important to avoid the developed mathematical model is troublesome and may yield inappropriate results. However, estimating the weights of the parameter or objective functions in the mathematical model is sometimes not easy in real situations, especially when the values are unavailable or difficult to decide. Additionally, various uncertainties include in the statistical data makes common mathematical analysis is not competent to deal with. Hence, this paper presents the Fuzzy Random Regression approach to determine the coefficient whereby statistical data used contain uncertainties namely, fuzziness and randomness. The proposed methods are able to provide coefficient information in the model setting and consideration of uncertainties in the evaluation process. The assessment of coefficient value is given by Weight Absolute Percentage Error of Fuzzy Decision. It clarifies the results between fuzzy decision and non-fuzzy decision that shows the distance of different between both approaches. Finally, a real-life application of production planning models is provided to illustrate the applicability of the proposed algorithms to a practical case study.

Multi-Attribute Decision Making in Contractor Selection Under Hybrid Uncertainty

2011 ◽  
Vol 15 (4) ◽  
pp. 465-472 ◽  
Author(s):  
Arbaiy Nureize ◽  
◽  
Junzo Watada ◽  
Keyword(s):  
Construction Industry ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Random Regression ◽  
Contractor Selection ◽  
Random Regression Model ◽  
Proposed Model ◽  
Multi Attribute Decision Making ◽  
Hybrid Uncertainty ◽  
Fuzzy Random

The successful of a construction industry project depends on contractor evaluation and selection. Further, human judgment and unknown evaluation risk make evaluation and selection increasingly complex. Such situations show that a contractor selection is influenced by multiple attributes that often have the hybrid uncertainty of fuzziness and probability. The objective of this study is therefore to propose a fuzzy random variable based multi-attribute decision scheme that enables us to solve such problems within the bounds of hybrid uncertainty by using a fuzzy random regression model. The proposed model is explained in examples and its usefulness is clarified. This decision model is facilitated in its use by evaluating alternatives and enables us to indicate the optimum choice in the presence of hybrid uncertainty.

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