The statistical analysis of compositional data
The statistical analysis of compositional data
Random effects in ordinal regression models
Computational Statistics & Data Analysis
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Machine Learning
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
International Journal of Approximate Reasoning
Support Vector Ordinal Regression
Neural Computation
On the scalability of ordered multi-class ROC analysis
Computational Statistics & Data Analysis
Statistical models for partial membership
Proceedings of the 25th international conference on Machine learning
Stochastic dominance-based rough set model for ordinal classification
Information Sciences: an International Journal
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Computational Statistics & Data Analysis
Bayesian Clustering of Fuzzy Feature Vectors Using a Quasi-Likelihood Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data analysis with fuzzy clustering methods
Computational Statistics & Data Analysis
Information Sciences: an International Journal
Editorial: Special issue on fuzzy sets in statistics
Computational Statistics & Data Analysis
An experimental study of different ordinal regression methods and measures
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Ordinal and nominal classification of wind speed from synoptic pressurepatterns
Engineering Applications of Artificial Intelligence
Exploitation of pairwise class distances for ordinal classification
Neural Computation
Kernelizing the proportional odds model through the empirical kernel mapping
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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As an extension of multi-class classification, machine learning algorithms have been proposed that are able to deal with situations in which the class labels are defined in a non-crisp way. Objects exhibit in that sense a degree of membership to several classes. In a similar setting, models are developed here for classification problems where an order relation is specified on the classes (i.e., non-crisp ordinal regression problems). As for traditional (crisp) ordinal regression problems, it is argued that the order relation on the classes should be reflected by the model structure as well as the performance measure used to evaluate the model. These arguments lead to a natural extension of the well-known proportional odds model for non-crisp ordinal regression problems, in which the underlying latent variable is not necessarily restricted to the class of linear models (by using kernel methods).