IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensembling neural networks: many could be better than all
Artificial Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analysis of diversity measures
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier Ensembles with a Random Linear Oracle
IEEE Transactions on Knowledge and Data Engineering
On diversity and accuracy of homogeneous and heterogeneous ensembles
International Journal of Hybrid Intelligent Systems
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
How do correlation and variance of base-experts affect fusion in biometric authentication tasks?
IEEE Transactions on Signal Processing
Accuracy/Diversity and Ensemble MLP Classifier Design
IEEE Transactions on Neural Networks
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Fuzzy integral based data fusion for protein function prediction
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Hierarchical multilabel protein function prediction using local neural networks
BSB'11 Proceedings of the 6th Brazilian conference on Advances in bioinformatics and computational biology
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Hierarchical multi-label classification using local neural networks
Journal of Computer and System Sciences
Computers & Mathematics with Applications
Intelligent Data Analysis
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Hierarchical classification problems gained increasing attention within the machine learning community, and several methods for hierarchically structured taxonomies have been recently proposed, with applications ranging from classification of web documents to bioinformatics. In this paper we propose a novel ensemble algorithm for multilabel, multi-path, tree-structured hierarchical classification problems based on the true path rule borrowed from the Gene Ontology. Local base classifiers, each specialized to recognize a single class of the hierarchy, exchange information between them to achieve a global "consensus" ensemble decision. A two-way asymmetric flow of information crosses the tree-structured ensemble: positive predictions for a node influence its ancestors, while negative predictions influence its offsprings. The resulting True Path Rule hierarchical ensemble is applied to the prediction of gene function in the yeast, using the FunCat taxonomy and biomolecular data obtained from high-throughput biotechnologies.