C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Making Better Use of Global Discretization
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
Decision trees for ordinal classification
Intelligent Data Analysis
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Pairwise Classification as an Ensemble Technique
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Using Hard Classifiers to Estimate Conditional Class Probabilities
ECML '02 Proceedings of the 13th European Conference on Machine Learning
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
Binary Decomposition Methods for Multipartite Ranking
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Rough Set Approach to Knowledge Discovery about Preferences
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
New Hybrid Recommender Approaches: An Application to Equity Funds Selection
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Conditional Density Estimation with Class Probability Estimators
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
The Journal of Machine Learning Research
Capturing the stars: predicting ratings for service and product reviews
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
Adapting decision DAGs for multipartite ranking
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Ordinal extreme learning machine
Neurocomputing
Citation-based journal ranks: The use of fuzzy measures
Fuzzy Sets and Systems
On exploiting hierarchical label structure with pairwise classifiers
ACM SIGKDD Explorations Newsletter
Multi-agent based classification using argumentation from experience
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Learning from label preferences
DS'11 Proceedings of the 14th international conference on Discovery science
Monotone instance ranking with MIRA
DS'11 Proceedings of the 14th international conference on Discovery science
Modeling personalized email prioritization: classification-based and regression-based approaches
Proceedings of the 20th ACM international conference on Information and knowledge management
Cost-Sensitive learning of SVM for ranking
ECML'06 Proceedings of the 17th European conference on Machine Learning
Classification of ordinal data using neural networks
ECML'05 Proceedings of the 16th European conference on Machine Learning
Learning partial ordinal class memberships with kernel-based proportional odds models
Computational Statistics & Data Analysis
Modeling the organoleptic properties of matured wine distillates
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Ordinal classification with monotonicity constraints
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
The discovery and use of ordinal information on attribute values in classifier learning
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Large-margin feature selection for monotonic classification
Knowledge-Based Systems
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
Neural network ensembles to determine growth multi-classes in predictive microbiology
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Learning Rule Ensembles for Ordinal Classification with Monotonicity Constraints
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Adaptive metric learning vector quantization for ordinal classification
Neural Computation
Evolutionary extreme learning machine for ordinal regression
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Ordinal and nominal classification of wind speed from synoptic pressurepatterns
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Decision trees: a recent overview
Artificial Intelligence Review
TellMyRelevance!: predicting the relevance of web search results from cursor interactions
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Exploitation of pairwise class distances for ordinal classification
Neural Computation
Hierarchical model for rank discrimination measures
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
An organ allocation system for liver transplantation based on ordinal regression
Applied Soft Computing
Improving ranking performance with cost-sensitive ordinal classification via regression
Information Retrieval
The data replication method for the classification with reject option
AI Communications
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Machine learning methods for classification problems commonly assume that the class values are unordered. However, in many practical applications the class values do exhibit a natural order--for example, when learning how to grade. The standard approach to ordinal classification converts the class value into a numeric quantity and applies a regression learner to the transformed data, translating the output back into a discrete class value in a post-processing step. A disadvantage of this method is that it can only be applied in conjunction with a regression scheme. In this paper we present a simple method that enables standard classification algorithms to make use of ordering information in class attributes. By applying it in conjunction with a decision tree learner we show that it outperforms the naive approach, which treats the class values as an unordered set. Compared to special-purpose algorithms for ordinal classification our method has the advantage that it can be applied without any modification to the underlying learning scheme.