Jaume Bacardit
Jaume Bacardit
Reader in Machine Learning, Newcastle University
Verified email at newcastle.ac.uk - Homepage
TitleCited byYear
KEEL: a software tool to assess evolutionary algorithms for data mining problems
J Alcalá-Fdez, L Sanchez, S Garcia, MJ del Jesus, S Ventura, JM Garrell, ...
Soft Computing 13 (3), 307-318, 2009
10482009
MRPR: A MapReduce solution for prototype reduction in big data classification
I Triguero, D Peralta, J Bacardit, S García, F Herrera
neurocomputing 150, 331-345, 2015
1732015
Pittsburgh genetics-based machine learning in the data mining era: Representations, generalization, and run-time
J Bacardit
PhD disertation, 2004
1272004
The eukaryotic N-end rule pathway: conserved mechanisms and diverse functions
DJ Gibbs, J Bacardit, A Bachmair, MJ Holdsworth
Trends in Cell Biology 24 (10), 603-611, 2014
1122014
Data mining in learning classifier systems: comparing XCS with GAssist
J Bacardit, M Butz
Learning Classifier Systems, 282-290, 2007
952007
Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology
AL Swan, A Mobasheri, D Allaway, S Liddell, J Bacardit
Omics: a journal of integrative biology 17 (12), 595-610, 2013
922013
ROSEFW-RF: the winner algorithm for the ECBDL’14 big data competition: an extremely imbalanced big data bioinformatics problem
I Triguero, S del Río, V López, J Bacardit, JM Benítez, F Herrera
Knowledge-Based Systems 87, 69-79, 2015
842015
Improving the scalability of rule-based evolutionary learning
J Bacardit, EK Burke, N Krasnogor
Memetic computing 1 (1), 55-67, 2009
802009
Functional network construction in Arabidopsis using rule-based machine learning on large-scale data sets
GW Bassel, E Glaab, J Marquez, MJ Holdsworth, J Bacardit
The Plant Cell 23 (9), 3101-3116, 2011
792011
Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data
E Glaab, J Bacardit, JM Garibaldi, N Krasnogor
PloS one 7 (7), e39932, 2012
782012
Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system
J Bacardit, JM Garrell
Learning Classifier Systems, 59-79, 2003
772003
Prediction of human population responses to toxic compounds by a collaborative competition
F Eduati, LM Mangravite, T Wang, H Tang, JC Bare, R Huang, T Norman, ...
Nature biotechnology 33 (9), 933, 2015
742015
Large‐scale data mining using genetics‐based machine learning
J Bacardit, X Llorà
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3 (1 …, 2013
722013
Evolving multiple discretizations with adaptive intervals for a pittsburgh rule-based learning classifier system
J Bacardit, JM Garrell
Genetic and Evolutionary Computation Conference, 1818-1831, 2003
722003
Speeding up the evaluation of evolutionary learning systems using GPGPUs
MA Franco, N Krasnogor, J Bacardit
Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010
552010
Automated alphabet reduction for protein datasets
J Bacardit, M Stout, J Hirst, A Valencia, R Smith, N Krasnogor
BMC bioinformatics 10 (1), 6, 2009
542009
Performance and efficiency of memetic pittsburgh learning classifier systems
J Bacardit, N Krasnogor
Evolutionary computation 17 (3), 307-342, 2009
532009
WeFold: a coopetition for protein structure prediction
GA Khoury, A Liwo, F Khatib, H Zhou, G Chopra, J Bacardit, LO Bortot, ...
Proteins: Structure, Function, and Bioinformatics 82 (9), 1850-1868, 2014
522014
Prediction of recursive convex hull class assignments for protein residues
M Stout, J Bacardit, JD Hirst, N Krasnogor
Bioinformatics 24 (7), 916-923, 2008
502008
Automated alphabet reduction method with evolutionary algorithms for protein structure prediction
J Bacardit, M Stout, JD Hirst, K Sastry, X Llorà, N Krasnogor
Proceedings of the 9th annual conference on Genetic and evolutionary …, 2007
492007
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Articles 1–20