C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Inducing classification and regression trees in first order logic
Relational Data Mining
Decision trees for ordinal classification
Intelligent Data Analysis
Journal of Artificial Intelligence Research
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
On the scalability of ordered multi-class ROC analysis
Computational Statistics & Data Analysis
Generalization Bounds for Some Ordinal Regression Algorithms
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System
Learning Classifier Systems
Supervised machine learning algorithms for protein structure classification
Computational Biology and Chemistry
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Block-quantized support vector ordinal regression
IEEE Transactions on Neural Networks
Using Data Mining for Wine Quality Assessment
DS '09 Proceedings of the 12th International Conference on Discovery Science
Cascade generalisation for ordinal problems
International Journal of Artificial Intelligence and Soft Computing
Ordinal regression with sparse Bayesian
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Learning with kernels and logical representations
Probabilistic inductive logic programming
Ordinal extreme learning machine
Neurocomputing
On exploiting hierarchical label structure with pairwise classifiers
ACM SIGKDD Explorations Newsletter
Software effort estimation based on optimized model tree
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Cost-Sensitive learning of SVM for ranking
ECML'06 Proceedings of the 17th European conference on Machine Learning
Neighborhood preserving ordinal regression
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
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This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression.