A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Optimizing search engines using clickthrough data
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Reducing multiclass to binary: a unifying approach for margin classifiers
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Gaussian Processes for Ordinal Regression
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Augmenting naive Bayes for ranking
ICML '05 Proceedings of the 22nd international conference on Machine learning
Prediction of Ordinal Classes Using Regression Trees
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Support Vector Ordinal Regression
Neural Computation
Letters: Convex incremental extreme learning machine
Neurocomputing
Kernel regression with order preferences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Bayesian hierarchical ordinal regression
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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COLT'05 Proceedings of the 18th annual conference on Learning Theory
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IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
An n-spheres based synthetic data generator for supervised classification
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
An organ allocation system for liver transplantation based on ordinal regression
Applied Soft Computing
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Recently, a new fast learning algorithm called Extreme Learning Machine (ELM) has been developed for Single-Hidden Layer Feedforward Networks (SLFNs) in G.-B. Huang, Q.-Y. Zhu and C.-K. Siew ''[Extreme learning machine: theory and applications,'' Neurocomputing 70 (2006) 489-501]. And, ELM has been successfully applied to many classification and regression problems. In this paper, the ELM algorithm is further studied for ordinal regression problems (named ORELM). We firstly proposed an encoding-based framework for ordinal regression which includes three encoding schemes: single multi-output classifier, multiple binary-classifications with one-against-all (OAA) decomposition method and one-against-one (OAO) method. Then, the SLFN was redesigned for ordinal regression problems based on the proposed framework and the algorithms are trained by the extreme learning machine in which input weights are assigned randomly and output weights can be decided analytically. Lastly widely experiments on three kinds of datasets were carried to test the proposed algorithm. The comparative results with such traditional methods as Gaussian Process for Ordinal Regression (ORGP) and Support Vector for Ordinal Regression (ORSVM) show that ORELM can obtain extremely rapid training speed and good generalization ability. Especially when the data set's scalability increases, the advantage of ORELM will become more apparent. Additionally, ORELM has the following advantages, including the capabilities of learning in both online and batch modes and handling non-linear data.