Results 31 to 40 of about 259,894 (334)

Active Learning for Imbalanced Ordinal Regression

IEEE Access, 2020
Ordinal regression (OR), also called ordinal classification, is a special multi-classification designed for problems with ordered classes. Imbalanced data hinders the performance of classification algorithms, especially for OR algorithms, as imbalanced ...
Jiaming Ge, Haiyan Chen, Dongfang Zhang, Xiaye Hou, Ligang Yuan   +4 more
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Convolutional Ordinal Regression Forest for Image Ordinal Estimation [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2022
Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different ...
Haiping Zhu, Hongming Shan, Yuheng Zhang, Lingfu Che, Xiaoyang Xu, Junping Zhang, Jianbo Shi, Fei-Yue Wang   +7 more
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Prediction of the Fundus Tessellation Severity With Machine Learning Methods

Frontiers in Medicine, 2022
PurposeTo predict the fundus tessellation (FT) severity with machine learning methods.MethodsA population-based cross-sectional study with 3,468 individuals (mean age of 64.6 ± 9.8 years) based on Beijing Eye Study 2011.
Lei Shao, Xiaomei Zhang, Teng Hu, Yang Chen, Chuan Zhang, Li Dong, Saiguang Ling, Zhou Dong, Wen Da Zhou, Rui Heng Zhang, Lei Qin, Wen Bin Wei   +11 more
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KLASIFIKASI TINGKAT KESEJAHTERAAN KELUARGA DI KECAMATAN SIDEMEN MENGGUNAKAN BOOTSTRAP AGGREGATING (BAGGING) REGRESI LOGISTIK ORDINAL

E-Jurnal Matematika, 2023
This research was conducted to determine the variables that have a significant impact on the stages of a well-off family in Sidemen Sub-district based on indicators obtained from the BKKBN and to classify the stages of a well-off family.
I GUSTI NGURAH SENTANA PUTRA, MADE SUSILAWATI, I PUTU WINADA GAUTAMA   +2 more
doaj   +1 more source

Smoothing in Ordinal Regression: An Application to Sensory Data

Stats, 2021
The so-called proportional odds assumption is popular in cumulative, ordinal regression. In practice, however, such an assumption is sometimes too restrictive.
Ejike R. Ugba, Daniel Mörlein, Jan Gertheiss   +2 more
doaj   +1 more source

Ordinal Choquistic Regression

Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology, 2013
We propose an extension of choquistic regression from the case of binary to the case of ordinal classification. Choquistic regression itself has been introduced recently as a generalization of conventional logistic regression. The basic idea of this method is to replace the linear function of predictor variables in the logistic regression model by the ...
Eyke Huellermeier, Ali Fallah Tehrani
openaire   +3 more sources

Ordinal Ridge Regression with Categorical Predictors [PDF]

, 2011
In multi-category response models categories are often ordered. In case of ordinal response models, the usual likelihood approach becomes unstable with ill-conditioned predictor space or when the number of parameters to be estimated is large relative to ...
Zahid, Faisal Maqbool
core   +1 more source

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

Using rank data to estimate health state utility models [PDF]

, 2006
In this paper we report the estimation of conditional logistic regression models for the Health Utilities Index Mark 2 and the SF-6D, using ordinal preference data.
Aki Tsuchiya, Anthony O’Hagan, Brazier, Brazier, Broome, Cairns, Christopher McCabe, Currim, Dolan, Dyer, Hausman, Jennifer Roberts, John Brazier, Katherine Stevens, Kind, Koop, Ljung, Lloyd, McFadden, Peter Gilks, Saigal, Thurstone, Torrance, Torrance, Torrance   +24 more
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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