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Computing a consensus of multilabeled trees [chapter]

Katharina T. Huber, Vincent Moulton, Andreas Spillner, Sabine Storandt, Radoslaw Suchecki
2012 2012 Proceedings of the Fourteenth Workshop on Algorithm Engineering and Experiments (ALENEX)  
In this paper we consider two challenging problems that arise in the context of computing a consensus of a collection of multilabeled trees, namely (1) selecting a compatible collection of clusters on  ...  a multiset from an ordered list of such clusters and (2) optimally refining high degree vertices in a multilabeled tree.  ...  of East Anglia.  ... 
doi:10.1137/1.9781611972924.9 dblp:conf/alenex/HuberMSSS12 fatcat:uggim7hamffh3hfrprso6vz6ce

True Path Rule Hierarchical Ensembles [chapter]

Giorgio Valentini
2009 Lecture Notes in Computer Science  
Local base classifiers, each specialized to recognize a single class of the hierarchy, exchange information between them to achieve a global "consensus" ensemble decision.  ...  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.  ...  Acknowledgments The author would like to thank the anonymous reviewers for their comments, and gratefully acknowledges partial support by the PASCAL2 Network of Excellence under EC grant no. 216886.  ... 
doi:10.1007/978-3-642-02326-2_24 fatcat:ud2aeugnqrcxrlkpt4h4igfmfm

Multilabel classification through random graph ensembles

Hongyu Su, Juho Rousu
2014 Machine Learning  
We present new methods for multilabel classification, relying on ensemble learning on a collection of random output graphs imposed on the multilabel, and a kernel-based structured output learner as the  ...  We compare our methods on a set of heterogeneous multilabel benchmark problems against the state-of-the-art machine learning approaches, including multilabel AdaBoost, convex multitask feature learning  ...  Acknowledgments The work was financially supported by Helsinki Doctoral Programme in Computer Science (Hecse), Academy of Finland grant 118653 (ALGODAN), IST Programme of the European Community under the  ... 
doi:10.1007/s10994-014-5465-9 fatcat:cmq6knacfffcdh7e3e7i7gkzqa

Dendroscope 3: An Interactive Tool for Rooted Phylogenetic Trees and Networks

Daniel H. Huson, Celine Scornavacca
2012 Systematic Biology  
It provides a number of methods for drawing and comparing rooted phylogenetic networks, and for computing them from rooted trees. The program can be used interactively or in command-line mode.  ...  Dendroscope 3 is a new program for working with rooted phylogenetic trees and networks.  ...  As already mentioned, the program provides a choice of algorithms for computing consensus trees and consensus networks from rooted phylogenetic trees, such as the strict, majority, and loose consensus  ... 
doi:10.1093/sysbio/sys062 pmid:22780991 fatcat:as7ihe7msbbxbolp6cogdwvzby

Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference

Nicolò Cesa-Bianchi, Matteo Re, Giorgio Valentini
2011 Machine Learning  
Gene function prediction is a complex multilabel classification problem with several distinctive features: the hierarchical relationships between functional classes, the presence of multiple sources of  ...  Besides classical top-down hierarchical multilabel ensemble methods, in our experiments we consider two recently proposed multilabel methods: one based on the approximation of the Bayesian optimal classifier  ...  The authors gratefully acknowledge partial support by the PASCAL2 Network of Excellence under EC grant no. 216886. This publication only reflects the authors' views.  ... 
doi:10.1007/s10994-011-5271-6 fatcat:5uvavmu6zjft3g7xvtc6sghsya

Multilabel Classification through Random Graph Ensembles [article]

Hongyu Su, Juho Rousu
2013 arXiv   pre-print
We present new methods for multilabel classification, relying on ensemble learning on a collection of random output graphs imposed on the multilabel and a kernel-based structured output learner as the  ...  We compare the methods against the state-of-the-art machine learning approaches on a set of heterogeneous multilabel benchmark problems, including multilabel AdaBoost, convex multitask feature learning  ...  Acknowledgments The work was financially supported by Helsinki Doctoral Programme in Computer Science (Hecse), Academy of Finland grant 118653 (ALGODAN), IST Programme of the European  ... 
arXiv:1310.8428v2 fatcat:kyuozkuoqzhtpmmlz5ubdqclke

Evaluating allopolyploid origins in strawberries (Fragaria) using haplotypes generated from target capture sequencing

Olga K. Kamneva, John Syring, Aaron Liston, Noah A. Rosenberg
2017 BMC Evolutionary Biology  
Genus Fragaria is one example of a set of plant taxa in which a range of ploidy levels is observed across species, but phylogenetic origins are unknown.  ...  We then identify putative hybridization events by analyzing gene tree topologies, and further test predicted hybridizations in a coalescence framework.  ...  The topologies of the multilabeled gene trees were summarized in a consensus format to identify putative hybridization events leading to the formation of polyploid species.  ... 
doi:10.1186/s12862-017-1019-7 pmid:28778145 pmcid:PMC5543553 fatcat:cwxgpqd7pnamncwh4tnyv7ltya

Simulation-Based Evaluation of Hybridization Network Reconstruction Methods in the Presence of Incomplete Lineage Sorting

Olga K Kamneva, Noah A Rosenberg
2017 Evolutionary Bioinformatics  
We report a comparative study of consensus, maximum parsimony, and maximum likelihood methods of species network reconstruction using gene trees simulated assuming a known species history.  ...  The more sophisticated likelihood methods, however, are affected by gene tree errors to a greater extent than are consensus and parsimony.  ...  Consensus. In consensus species network inference, a collection of gene trees is summarized in a form of a network.  ... 
doi:10.1177/1176934317691935 pmid:28469378 pmcid:PMC5395256 fatcat:zt6czps6szaw3lb2qc2fatdbty

Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks

Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John Shawe-Taylor
2014 Neural Information Processing Systems  
We also show that a small random sample of these output trees can attain a significant fraction of the margin obtained by the complete graph and we provide conditions under which we can perform tractable  ...  We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees.  ...  We use ten multilabel datasets from [5] . Following [5] , MAM is constructed with 180 tree based learners, and for MMCRF a consensus graph is created by pooling edges from 40 trees.  ... 
dblp:conf/nips/MarchandSMRS14 fatcat:tczkda32enfdni2l4fzaoigi4i

Local and global feature selection for multilabel classification with binary relevance

André Melo, Heiko Paulheim
2017 Artificial Intelligence Review  
they break a global multilabel classification problem into a set of smaller binary or multiclass classification problems, which are well understood and extensively researched.  ...  We empirically compare their performance on various flat and hierarchical multilabel datasets of different application domains.  ...  of Knowledge Graphs on the Web).  ... 
doi:10.1007/s10462-017-9556-4 fatcat:y2ho4l5ohjhk7h6frrmmtk6tlq

Triplet-based similarity score for fully multi-labeled trees with poly-occurring labels

Simone Ciccolella, Giulia Bernardini, Luca Denti, Paola Bonizzoni, Marco Previtali, Gianluca Della Vedova, Arne Elofsson
2020 Bioinformatics  
Moreover, none of the known similarity measures is able to manage mutations occurring multiple times in the tree, a circumstance often occurring in real cases.  ...  Moreover, a comparison of MP3 with other measures shows that it is able to classify correctly similar and dissimilar trees, both on simulated and on real data.  ...  Extension to fully labeled trees and multilabeled trees A tree T on a set V T of n nodes is fully labeled by a set kðTÞ of labels if there is a bijection N : kðTÞ ! V T .  ... 
doi:10.1093/bioinformatics/btaa676 pmid:32730595 pmcid:PMC8055217 fatcat:zj64usox5zbp5ojv4lxup6vdbm


Chandrasekar Ramachandran, Rahul Malik, Xin Jin, Jing Gao, Klara Nahrstedt, Jiawei Han
2009 Proceedings of the seventeen ACM international conference on Multimedia - MM '09  
We then combine the results of trained classifiers and clustering algorithms using a novel heuristic consensus learning algorithm which as a whole performs better than each individual learning model.  ...  In this paper we propose VideoMule, a consensus learning approach for multi-label video classification from noisy user-generated videos.  ...  SYSTEM OVERVIEW Our objective in this work is to find the optimal multilabel class for a given test video through consensus learning.  ... 
doi:10.1145/1631272.1631397 dblp:conf/mm/RamachandranMJGNH09 fatcat:y546dnxvgjaxlg37y7fnkrj3f4

Adapting non-hierarchical multilabel classification methods for hierarchical multilabel classification

Ricardo Cerri, André Carlos P. L. F. de Carvalho, Alex A. Freitas
2011 Intelligent Data Analysis  
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.  ...  This article proposes two new hierarchical multilabel classification methods based on the well-known local approach for hierarchical classification.  ...  for the Clus-HMC program and the datasets used, Amanda Clare for the HC4.5 program, and Thiago Covões for the codes of the statistical tests.  ... 
doi:10.3233/ida-2011-0500 fatcat:xu5msa7pbbb7fgtlfb4ev6xu3e

Hierarchical Ensemble Methods for Protein Function Prediction

Giorgio Valentini
2014 ISRN Bioinformatics  
Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple  ...  According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a "consensus" ensemble decision, taking  ...  the reviewers for their comments and suggestions and acknowledges partial support from the PRIN project "Automi e linguaggi formali: aspetti matematici e applicativi", funded by the italian Ministry of  ... 
doi:10.1155/2014/901419 pmid:25937954 pmcid:PMC4393075 fatcat:i6w56fpbqnekjozm2kdpik635e

sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification [article]

Gabriel Bénédict, Vincent Koops, Daan Odijk, Maarten de Rijke
2021 arXiv   pre-print
We propose a loss function, sigmoidF1, which is an approximation of the F1 score that (1) is smooth and tractable for stochastic gradient descent, (2) naturally approximates a multilabel metric, and (3  ...  Current models formulate a reduction of the multilabel setting into either multiple binary classifications or multiclass classification, allowing for the use of existing loss functions (sigmoid, cross-entropy  ...  Metrics: evaluation at inference time There is consensus on the use of a confusion matrix and ranking metrics to evaluate multilabel classification models (at inference time) [6, 39, 75] .  ... 
arXiv:2108.10566v2 fatcat:rb7nr7vwb5b4tcubgxpcyebfve
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