Machine Learning
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
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
Structured output prediction of anti-cancer drug activity
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
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We present a new method for drug bioactivity classification based on learning an ensemble of multi-task classifiers. As the base classifiers of the ensemble we use Max-Margin Conditional Random Field (MMCRF) models, which have previously obtained the state-of-the-art accuracy in this problem. MMCRF relies on a graph structure coupling the set of tasks together, and thus turns the multi-task learning problem into a graph labeling problem. In our ensemble method the graphs of the base classifiers are random, constructed by random pairing or random spanning tree extraction over the set of tasks. We compare the ensemble approaches on datasets containing the cancer inhibition potential of drug-like molecules against 60 cancer cell lines. In our experiments we find that ensembles based on random graphs surpass the accuracy of single SVM as well as a single MMCRF model relying on a graph built from auxiliary data.