Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning - Special issue on learning with probabilistic representations
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Text classification in a hierarchical mixture model for small training sets
Proceedings of the tenth international conference on Information and knowledge management
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Hierarchical multi-label prediction of gene function
Bioinformatics
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Incremental Algorithms for Hierarchical Classification
The Journal of Machine Learning Research
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Boosting multi-label hierarchical text categorization
Information Retrieval
Decision trees for hierarchical multi-label classification
Machine Learning
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Multi-label Hierarchical Classification of Protein Functions with Artificial Immune Systems
BSB '08 Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Top-Down Hierarchical Ensembles of Classifiers for Predicting G-Protein-Coupled-Receptor Functions
BSB '08 Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
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
Refined experts: improving classification in large taxonomies
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
True Path Rule Hierarchical Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Exploiting known taxonomies in learning overlapping concepts
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Open-source machine learning: R meets Weka
Computational Statistics - Proceedings of DSC 2007
Comparing several approaches for hierarchical classification of proteins with decision trees
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
TreeBoost.MH: a boosting algorithm for multi-label hierarchical text categorization
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
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In most classification problems, a classifier assigns a single class to each instance and the classes form a flat non-hierarchical structure, without superclasses or subclasses. In hierarchical multilabel classification problems, the classes are hierarchically structured, with superclasses and subclasses, and instances can be simultaneously assigned to two or more classes at the same hierarchical level. This article proposes two new hierarchical multilabel classification methods based on the well-known local approach for hierarchical classification. The methods are compared with two global methods and one well-known local binary classification method from the literature. The proposed methods presented promising results in experiments performed with bioinformatics datasets.