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

DOI10.1007/s10994-011-5271-6zbMath1243.68234OpenAlexW2108892946WikidataQ59290307 ScholiaQ59290307MaRDI QIDQ439045

Matteo Re, Nicolò Cesa-Bianchi, Giorgio Valentini

Publication date: 31 July 2012

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-011-5271-6

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Genetics and epigenetics (92D10)

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Uses Software

• SHOGUN
• ML-KNN
• PSI-BLAST
• BLAST
• BioGRID
• Pfam
• HCGene
• GeneMANIA

Cites Work

• On label dependence and loss minimization in multi-label classification
• ML-KNN: A lazy learning approach to multi-label learning
• Decision trees for hierarchical multi-label classification
• Structured prediction by joint kernel support estimation
• A note on Platt's probabilistic outputs for support vector machines
• Predicting Gene Function using Predictive Clustering Trees
• Decision templates for multiple classifier fusion: An experimental comparison
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