C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Learning hierarchical multi-category text classification models
ICML '05 Proceedings of the 22nd international conference on Machine learning
Hierarchical multi-label prediction of gene function
Bioinformatics
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Hierarchical multi-classification with predictive clustering trees in functional genomics
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Decision trees for hierarchical multi-label classification
Machine Learning
Ensembles of Multi-Objective Decision Trees
ECML '07 Proceedings of the 18th European conference on Machine Learning
Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees
DS '08 Proceedings of the 11th International Conference on Discovery Science
A Hierarchical Classification Ant Colony Algorithm for Predicting Gene Ontology Terms
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Hierarchical Core Vector Machines for Network Intrusion Detection
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Beam search induction and similarity constraints for predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Mr.KNN: soft relevance for multi-label classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A simple approach to incorporate label dependency in multi-label classification
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
S.cerevisiae complex function prediction with modular multi-relational framework
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Hierarchical annotation of medical images
Pattern Recognition
Hierarchical multilabel protein function prediction using local neural networks
BSB'11 Proceedings of the 6th Brazilian conference on Advances in bioinformatics and computational biology
Incorporating label dependency into the binary relevance framework for multi-label classification
Expert Systems with Applications: An International Journal
Evolving multi-label classification rules with gene expression programming: a preliminary study
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A genetic algorithm for Hierarchical Multi-Label Classification
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Inducing decision trees with an ant colony optimization algorithm
Applied Soft Computing
Tree ensembles for predicting structured outputs
Pattern Recognition
Multi-Label Classification Method for Multimedia Tagging
International Journal of Multimedia Data Engineering & Management
Hierarchical multi-label classification using local neural networks
Journal of Computer and System Sciences
Fundamenta Informaticae - Concurrency, Specification and Programming
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Hierarchical multilabel classification (HMC) is a variant of classification where instances may belong to multiple classes organized in a hierarchy. The task is relevant for several application domains. This paper presents an empirical study of decision tree approaches to HMC in the area of functional genomics. We compare learning a single HMC tree (which makes predictions for all classes together) to learning a set of regular classification trees (one for each class). Interestingly, on all 12 datasets we use, the HMC tree wins on all fronts: it is faster to learn and to apply, easier to interpret, and has similar or better predictive performance than the set of regular trees. It turns out that HMC tree learning is more robust to overfitting than regular tree learning.