Regression Models for Performance Ranking of Configurable Systems: A Comparative Study

2020 ◽  
pp. 243-258
Author(s):  
Yuntianyi Chen ◽  
Yongfeng Gu ◽  
Lulu He ◽  
Jifeng Xuan
Keyword(s):  
Comparative Study ◽  
Regression Models ◽  
Configurable Systems ◽  
Performance Ranking

Application and comparative study of new optimization method for performance ranking of friction materials

2017 ◽  
Vol 232 (2) ◽  
pp. 143-154 ◽  
Author(s):  
Vishal Mahale ◽  
Jayashree Bijwe ◽  
Sujeet Sinha
Keyword(s):  
Decision Making ◽  
Comparative Study ◽  
Decision Model ◽  
Evaluation Method ◽  
Multiple Criteria Decision Making ◽  
Optimization Method ◽  
Friction Material ◽  
Multiple Criteria ◽  
Friction Materials ◽  
Performance Ranking

Good friction materials should satisfy diverse and contradictory performance requirements such as adequate friction ( µ ≈ 0.35–0.45), resistance to wear, fade, squeal, judder, etc. in consort with good recovery and less noise producing tendency. To achieve center point of all these conflicting criteria and selection of best overall performing friction material is multiple criteria decision making (MCDM) problem and very difficult task. Decision maker can easily make decision with single criteria without the help of any optimization tool by maximizing beneficial criteria and minimizing non-beneficial criteria. However, it is extremely challenging task if decision making involves several number of conflicting criteria. Few techniques are reported in the literature such as ‘multiple criteria decision model’, ‘Multi-attribute decision model’, ‘extension evaluation method’ (EEM), etc. for performance ranking of friction materials. However, the simplicity, reliability, applicability, time devoted for the analysis, etc. are always most important aspects of selecting a right tool for the analysis. In this paper application of a technique ‘multiple objective optimization on the basis of ratio analysis’ (MOORA) has been first time employed for performance ranking of friction materials. A comparative study of MOORA and currently used methods MCDM and EEM are also presented. MOORA proved to be the best tool based on the criteria such as simple to use, fast, flexible, and efficient one.

Balloon ascent prediction: Comparative study of analytical, fuzzy and regression models

2019 ◽  
Vol 64 (1) ◽  
pp. 252-270 ◽  
Author(s):  
Kanika Garg ◽  
M. Reza Emami
Keyword(s):  
Comparative Study ◽  
Regression Models

A comparative study of robust designs for M-estimated regression models

2010 ◽  
Vol 54 (6) ◽  
pp. 1683-1695 ◽  
Author(s):  
Douglas P. Wiens ◽  
Eden K.H. Wu
Keyword(s):  
Comparative Study ◽  
Regression Models ◽  
Robust Designs

scholarly journals That takes the BISCUIT: A comparative study of predictive accuracy and parsimony of four statistical learning techniques in personality data, with data missingness conditions

2020 ◽  
Author(s):  
Lorien Grey Elleman ◽  
Sarah K McDougald ◽  
David M Condon ◽  
William Revelle
Keyword(s):  
Random Forest ◽  
Comparative Study ◽  
Statistical Learning ◽  
Big Five ◽  
Regression Models ◽  
Predictive Accuracy ◽  
Complete Data ◽  
Elastic Net ◽  
Learning Techniques ◽  
Statistical Learning Techniques

The predictive accuracy of personality-criterion regression models may be improved with statistical learning (SL) techniques. This study introduced a novel SL technique, BISCUIT (Best Items Scale that is Cross-validated, Unit-weighted, Informative and Transparent). The predictive accuracy and parsimony of BISCUIT was compared with three established SL techniques (the lasso, elastic net, and random forest) and regression using two sets of scales, for five criteria, across five levels of data missingness. BISCUIT’s predictive accuracy was competitive with other SL techniques at higher levels of data missingness. BISCUIT most frequently produced the most parsimonious SL model. The elastic net and lasso dominated other techniques in terms of predictive accuracy with complete data and in conditions with up to 50% data missingness. In terms of predictive accuracy, regression using 27 narrow traits was an intermediate choice. For most criteria and levels of data missingness, regression using the Big Five had the worst predictive accuracy. Overall, loss in predictive accuracy due to data missingness was modest, even at 90% data missingness. Findings suggest that personality researchers should consider incorporating planned data missingness and SL techniques into their designs and analyses.

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