Results 21 to 30 of about 96,202 (258)
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 +4 more
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Support Vector Ordinal Regression [PDF]
Neural Computation, 2007 In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.
Wei Chu, S. Sathiya Keerthi
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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 +11 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 +7 more
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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 +2 more
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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 +2 more
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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 ...
Tehrani, Ali Fallah, Hüllermeier, Eyke
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Geographically Weighted Probit Ordinal Regression Model Estimation [PDF]
E3S Web of ConferencesGeographically 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 +2 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
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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
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