Ordinal Regression
On the Consistency of Ordinal Regression Methods
... behind ordinal regression methods, at least compared to that of binary and multiclass ...of ordinal regression remains ...of ordinal regression methods that parallels the ones ...
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Multivariate decoding of brain images using ordinal regression
... multivariate ordinal regression framework which inherently models the natural ordering in the data labels and can establish whether a collection of brain regions are ordinally related across a ...previous ...
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Learning to Predict One or More Ranks in Ordinal Regression Tasks
... The approach presented in this paper explores a new kind of predictions in ordinal regression. We shall build hypotheses that try to predict the true rank for an entry, but when the classification is ...
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Multivariate ordinal regression models: an analysis of corporate credit ratings
... Correlated ordinal data typically arises from multiple measurements on a collection of ...an ordinal scale, we consider mul- tivariate ordinal regression models with a latent variable ...
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Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution
... novel regression coe ffi cient features represent- ing two patches, and their corresponding label is the subpixel shift between ...ranking/ordinal regression [5] to per- form subpixel shift ...
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Predicting progression of Alzheimer's disease using ordinal regression
... Ordinal regression provides a single summary index of the state of disease progression - the ORCHID ...evaluated ordinal regression using both datasets and achieved a balanced accuracy of ...
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Robust ordinal regression in preference learning and ranking
... Abstract Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches de- signed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from ...
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Projection based ensemble learning for ordinal regression
... 3) Large-scale datasets and interpretability: Once the per- formance of the proposed method has been extensively val- idated making statistical comparisons to other state-of-the- art methodologies for different measures ...
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Multi Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media
... E.g., predicting extreme-left instead of extreme- right, since the model is encouraged by the aux- iliary tasks to learn the correct polarity, regard- less of its intensity. We can see that factuality is not very useful ...
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A new method for error degree estimation in numerical weather prediction via MKDA based ordinal regression
... method, which can seamlessly combine the spectral embedding of unlabeled data and manifold regularized least squares to learn non-parametric kernels efficiently [18]. From the above discussion, we focus on the KDA- based ...
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Comparison of the accuracy of beta-binomial, multinomial, dirichlet-multinomial, and ordinal regression in modelling quality of life data
... Methods & Materials: In this cross-sectional study we collected the quality of life data from 511 healthy women in Qazvin, Iran. The data were used to compare accuracy of beta- binomial model and with some other ...
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Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations
... and ordinal regression anal- ysis also led to consultation with participating resident radiologists highlighting the importance of image quality criteria for CT head imaging, which could be weighted ...
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Selection of a Representative Value Function for Robust Ordinal Regression in Group Decision Making
... for multiple criteria ranking problems, or possible-necessary assignments for multiple criteria sorting problems. Generally, we wish to emphasize the difference between values of alternatives if one can observe the ...
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Inducing probability distributions on the set of value functions by Subjective Stochastic Ordinal Regression
... Abstract: Ordinal regression methods of Multiple Criteria Decision Aiding (MCDA) take into account one, several, or all value functions compatible with the indirect pref- erence information provided by the ...
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Cost-sensitive adaboost algorithm for ordinal regression based on extreme learning machine
... IEEE TRANSACTIONS ON CYBERNETICS 1 A Cost-Sensitive AdaBoost algorithm for Ordinal Regression based on Extreme Learning Machine Annalisa Riccardi, Francisco Fern´andez-Navarro, Member IE[r] ...
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Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding
... We have proposed a new word embedding model, which is based on ordinal regression. The input to our model consists of a number of rankings, cap- turing how strongly each word is related to each context word ...
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ORCA: A Matlab/Octave Toolbox for Ordinal Regression
... (Ordinal Regression and Classification Algorithms), a Matlab/Octave framework that gathers an extensive collection of recent ordinal machine learning methods and ordinal performance ...to ...
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A measurement error model for binary and ordinal regression
... binary and ordinal regression to data relating the occurrence of respiratory symptoms in children to indoor concentrations of nitrogen dioxide (N0.. 2 ).[r] ...
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Robust estimation for ordinal regression.
... To the best of our knowledge, this is the first paper where robustness for ordinal regression is studied. First we study the robustness of the classical Maximum Likelihood Estimator, and show that the slope ...
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Deep Ordinal Regression for Pledge Specificity Prediction
... In this work we present a new dataset of election campaign texts, annotated with pledge specificity on a fine-grained scale. We study the use of deep ordinal regression approaches using an auxiliary ...
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