Ensembles of extremely randomized predictive clustering trees for predicting structured outputs
- 17 August 2020
- journal article
- research article
- Published by Springer Nature in Machine Learning
- Vol. 109 (11) , 2213-2241
Abstract
No abstract availableKeywords
Funding Information
- Horizon 2020 Framework Programme (ICT-2013-612944 MAESTRA, H2020-688797 TOREADOR)
- Horizon 2020 Framework Programme (the Human Brain Broject HBP SGA1 and SGA2)
- The Slovenian Research and Innovation Agency (J2-9230, young researcher grant)
- The Slovenian Research and Innovation Agency (P2-0103)
This publication has 38 references indexed in Scilit:
- Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function predictionBMC Bioinformatics, 2013
- A large-scale evaluation of computational protein function predictionNature Methods, 2013
- Phyletic Profiling with Cliques of Orthologs Is Enhanced by Signatures of Paralogy RelationshipsPLOS Computational Biology, 2013
- A survey of hierarchical classification across different application domainsData Mining and Knowledge Discovery, 2010
- Multi-output regression on the output manifoldPattern Recognition, 2009
- Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiaegene functionGenome Biology, 2008
- ML-KNN: A lazy learning approach to multi-label learningPattern Recognition, 2007
- Future trends in data miningData Mining and Knowledge Discovery, 2007
- Adaptive Multivariate Ridge RegressionThe Annals of Statistics, 1980
- A Comparison of Alternative Tests of Significance for the Problem of $m$ RankingsThe Annals of Mathematical Statistics, 1940