The Predictive Performance Evaluation of Biased Regression Predictors With Correlated Errors

2015 ◽  
Vol 34 (5) ◽  
pp. 364-378 ◽  
Author(s):  
Issam Dawoud ◽  
Selahattin Kaçiranlar
Keyword(s):  
Performance Evaluation ◽  
Predictive Performance ◽  
Correlated Errors ◽  
Biased Regression

A Survey on Graphical Methods for Classification Predictive Performance Evaluation

2011 ◽  
Vol 23 (11) ◽  
pp. 1601-1618 ◽  
Author(s):  
Ronaldo C. Prati ◽  
Gustavo E. A. P. A. Batista ◽  
Maria Carolina Monard
Keyword(s):  
Performance Evaluation ◽  
Predictive Performance ◽  
Graphical Methods

Evaluation of the predictive performance of biased regression estimators

1985 ◽  
Vol 4 (2) ◽  
pp. 153-163 ◽  
Author(s):  
David J. Friedman ◽  
Douglas C. Montgomery
Keyword(s):  
Predictive Performance ◽  
Regression Estimators ◽  
Biased Regression

scholarly journals Performance Evaluation of Automobile Fuel Consumption Using a Fuzzy-Based Granular Model with Coverage and Specificity

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1480 ◽  
Author(s):  
Chan-Uk Yeom ◽  
Keun-Chang Kwak
Keyword(s):  
Performance Evaluation ◽  
Evaluation Method ◽  
Ground Truth ◽  
Predictive Performance ◽  
Linguistic Context ◽  
Information Transformation ◽  
Fuzzy C Means Clustering ◽  
The Difference ◽  
Performance Evaluation Method ◽  
Output Space

The predictive performance of different granular models (GMs) was compared and analyzed for methods that evenly divide linguistic context in information granulation-based GMs and perform flexible partitioning. GMs are defined by input and output space information transformations using context-based fuzzy C-means clustering. The input space information transformation is directly induced by the output space context. Usually, the output space context is evenly divided. In this paper, the linguistic context was flexibly divided by stochastically distributing data in the output space. Unlike most fuzzy models, this GM yielded information segmentation. Their performance is usually evaluated using the root mean square error, which utilizes the difference between the model’s output and ground truth. However, this is inadequate for the performance evaluation of information innovation-based GMs. Thus, the GM performance was compared and analyzed using the linguistic context partitioning by selecting the appropriate performance evaluation method for the GM. The method was augmented by the coverage and specificity of the GMs output as the performance index. For the GM validation, its performance was compared and analyzed using the auto MPG dataset. The GM with flexible partitioning of linguistic context performed better. Performance evaluation using the coverage and specificity of the membership function was validated.

Performance Evaluation of Interactive Systems with Interactive Cooperative Objects Models

2018 ◽  
Author(s):  
Célia Martinie ◽  
Philippe Palanque ◽  
Camille Fayollas
Keyword(s):  
Performance Evaluation ◽  
Human Computer Interaction ◽  
Empirical Studies ◽  
Predictive Performance ◽  
Interactive Systems ◽  
Interactive System ◽  
Formal Models ◽  
Evaluation Approaches ◽  
User Actions

Arguments to support validity of most contributions in the field of human–computer interaction are based on detailed results of empirical studies involving cohorts of tested users confronted with a set of tasks performed on a prototype version of an interactive system. This chapter presents how the Interactive Cooperative Objects (ICO) formal models of the entire interactive system can support predictive and summative performance evaluation activities by exploiting the models. Predictive performance evaluation is supported by ICO formal models of interactive systems enriched with perceptive, cognitive, and motoric information about the users. Summative usability evaluation is addressed at the level of the software system, which is able to exhaustively log all the user actions performed on the interactive system The articulation of these two evaluation approaches is demonstrated on a case study from the avionics domain with a step-by-step tutorial on how to apply the approach.

Fine Particle Mass Monitoring with Low-Cost Sensors: Corrections and Long-Term Performance Evaluation

2019 ◽  
Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Provat K. Saha ◽  
Allen L. Robinson ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Fine Particle ◽  
Low Cost ◽  
Particle Mass ◽  
Long Term Performance ◽  
Term Performance
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