Results 21 to 30 of about 96,053 (159)

Geographically Weighted Probit Ordinal Regression Model Estimation [PDF]

E3S Web of Conferences
Geographically Weighted Probit Ordinal Regression (GWPOR) is a combined method between Geographically Weighted Regression and Probit Ordinal Regression. This study estimates the percentage of poor people using the GWPOR method.
Kurniawan Muh. Idham, Purhadi, Rahayu Santi Puteri   +2 more
doaj   +1 more source

Twitter Sentiment Analysis Based on Ordinal Regression

IEEE Access, 2019
In recent years, research on Twitter sentiment analysis, which analyzes Twitter data (tweets) to extract user sentiments about a topic, has grown rapidly. Many researchers prefer the use of machine learning algorithms for such analysis.
Shihab Elbagir Saad, Jing Yang
doaj   +1 more source

Ordinal regression model for pea seed mass

Die Bodenkultur, 2017
The development of seeds at various positions in the pod is asynchronous. Thus, the differences of seed dry mass production because of environmental conditions may depend on the cultivar type, type of inoculants and interrelations between seeds per pod ...
Klimek-Kopyra Agnieszka, Strojny Jacek, Zając Tadeusz, Ślizowska Anna, Klimešova Jana, Neugschwandtner Reinhard W.   +5 more
doaj   +1 more source

Simultaneous optimization of quantitative and ordinal responses using Taguchi method [PDF]

International Journal of Research in Industrial Engineering, 2018
In the real world, the overall quality of a product is often represented partly by the measured values of some quantitative variables and partly by the observed values of some ordinal variables.
S. Pal, S. Gauri
doaj   +1 more source

Evaluation Measures for Ordinal Regression

2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009
Ordinal regression (OR -- also known as ordinal classification) has received increasing attention in recent times, due to its importance in IR applications such as learning to rank and product review rating. However, research has not paid attention to the fact that typical applications of OR often involve datasets that are highly imbalanced.
Baccianella S, Esuli A, Sebastiani F
openaire   +3 more sources

Regression with Ordered Predictors via Ordinal Smoothing Splines

Frontiers in Applied Mathematics and Statistics, 2017
Many applied studies collect one or more ordered categorical predictors, which do not fit neatly within classic regression frameworks. In most cases, ordinal predictors are treated as either nominal (unordered) variables or metric (continuous) variables ...
Nathaniel E. Helwig, Nathaniel E. Helwig
doaj   +1 more source

Regressão logística ordinal em estudos epidemiológicos Regresión logística ordinal en estudios epidemiológicos Ordinal logistic regression in epidemiological studies

Revista de Saúde Pública, 2009
Os modelos de regressão logística ordinal vêm sendo aplicados com sucesso na análise de estudos epidemiológicos. Entretanto, a verificação da adequação de cada modelo tem recebido atenção limitada.
Mery Natali Silva Abreu, Arminda Lucia Siqueira, Waleska Teixeira Caiaffa   +2 more
doaj   +1 more source

Dwell Time Estimation of Import Containers as an Ordinal Regression Problem

Applied Sciences, 2021
The optimal stacking of import containers in a terminal reduces the reshuffles during the unloading operations. Knowing the departure date of each container is critical for optimal stacking.
Laidy De Armas Jacomino, Miguel Angel Medina-Pérez, Raúl Monroy, Danilo Valdes-Ramirez, Carlos Morell-Pérez, Rafael Bello   +5 more
doaj   +1 more source

Predictive Factors for Pelvic Organ Prolapse (POP) in Iranian Women’s: An Ordinal Logistic Approch [PDF]

Journal of Clinical and Diagnostic Research, 2014
Introduction: To investigate the predictors factors of Pelvic Organ Prolapse (POP) in Iranian women by using ordinal logistic regression. Materials and Methods: The role of risk factors of POP was evaluated among 365 patients attending in two public ...
Ashraf Direkvand-Moghadam, Zeinab Ghazanfari, Kourosh Sayehmiri   +2 more
doaj   +1 more source

Feature selection for ordinal regression [PDF]

Proceedings of the 2010 ACM Symposium on Applied Computing, 2010
Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. This problem is receiving increasing attention from the sentiment analysis and opinion mining community, due to the importance of automatically rating ...
Baccianella S, Esuli A, Sebastiani F
openaire   +2 more sources