An integrated probabilistic model for functional prediction of proteins
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Hierarchical multi-label prediction of gene function
Bioinformatics
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Model-shared subspace boosting for multi-label classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting multi-label hierarchical text categorization
Information Retrieval
Decision trees for hierarchical multi-label classification
Machine Learning
Multi-class Boosting with Class Hierarchies
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Using the Gene Ontology hierarchy when predicting gene function
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Multi-label learning by exploiting label dependency
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multi-resolution boosting for classification and regression problems
Knowledge and Information Systems
A Bayesian integration model for improved gene functional inference from heterogeneous data sources
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Hierarchical multi-label classification is a variant of traditional classification in which the instances can belong to several labels, that are in turn organized in a hierarchy. Existing hierarchical multi-label classification algorithms ignore possible correlations between the labels. Moreover, most of the current methods predict instance labels in a "flat" fashion without employing the ontological structures among the classes. In this paper, we propose HiBLADE (Hierarchical multi-label Boosting with LAbel DEpendency), a novel algorithm that takes advantage of not only the pre-established hierarchical taxonomy of the classes, but also effectively exploits the hidden correlation among the classes that is not shown through the class hierarchy, thereby improving the quality of the predictions. According to our approach, first, the pre-defined hierarchical taxonomy of the labels is used to decide upon the training set for each classifier. Second, the dependencies of the children for each label in the hierarchy are captured and analyzed using Bayes method and instance-based similarity. Our experimental results on several real-world biomolecular datasets show that the proposed method can improve the performance of hierarchical multi-label classification.