A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Support vector machines classification with a very large-scale taxonomy
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Intelligent Data Analysis
Learning to Classify Ordinal Data: The Data Replication Method
The Journal of Machine Learning Research
Multilabel classification via calibrated label ranking
Machine Learning
Decision trees for hierarchical multi-label classification
Machine Learning
Efficient Pairwise Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
A Study of Hierarchical and Flat Classification of Proteins
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning from label preferences
DS'11 Proceedings of the 14th international conference on Discovery science
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The goal of this work was to test whether the performance of a regular pairwise classifier can be improved when additional information about the hierarchical class structure is added to the training sets. Somewhat surprisingly, the additional information seems to hurt the performance. We explain this with the fact that the structure of the class hierarchy is not reflected in the distribution of the instances.