TY - GEN
T1 - On the homogenization of data from two laboratories using Genetic Programming
AU - Moreno-Torres, Jose G.
AU - Llorà, Xavier
AU - Goldberg, David E.
AU - Bhargava, Rohit
N1 - Funding Information:
Jose García Moreno-Torres was supported by a scholarship from ‘Obra Social la Caixa’ and is currently supported by a FPU grant from the Ministerio de Educación y Ciencia of the Spanish Government and the KEEL project. Rohit Bhargava would like to acknowledge collaborators over the years, especially Dr. Stephen M. Hewitt and Dr. Ira W. Levin of the National Institutes of Health, for numerous useful discussions and guidance. Funding for this work was provided in part by University of Illinois Research Board and by the Department of Defense Prostate Cancer Research Program. This work was also funded in part by the National Center for Supercomputing Applications and the University of Illinois, under the auspices of the NCSA/UIUC faculty fellows program.
Funding Information:
Jose Garćıa Moreno-Torres was supported by a scholarship from ‘Obra Social la Caixa’ and is currently supported by a FPU grant from the Ministerio de Educación y Ciencia of the Spanish Government and the KEEL project. Rohit Bhargava would like to acknowledge collaborators over the years, especially Dr. Stephen M. Hewitt and Dr. Ira W. Levin of the National Institutes of Health, for numerous useful discussions and guidance. Funding for this work was provided in part by University of Illinois Research Board and by the Department of Defense Prostate Cancer Research Program. This work was also funded in part by the National Center for Supercomputing Applications and the University of Illinois, under the auspices of the NCSA/UIUC faculty fellows program.
PY - 2010
Y1 - 2010
N2 - In experimental sciences, diversity tends to difficult predictive models' proper generalization across data provided by different laboratories. Thus, training on a data set produced by one lab and testing on data provided by another lab usually results in low classification accuracy. Despite the fact that the same protocols were followed, variability on measurements can introduce unforeseen variations that affect the quality of the model. This paper proposes a Genetic Programming based approach, where a transformation of the data from the second lab is evolved driven by classifier performance. A real-world problem, prostate cancer diagnosis, is presented as an example where the proposed approach was capable of repairing the fracture between the data of two different laboratories.
AB - In experimental sciences, diversity tends to difficult predictive models' proper generalization across data provided by different laboratories. Thus, training on a data set produced by one lab and testing on data provided by another lab usually results in low classification accuracy. Despite the fact that the same protocols were followed, variability on measurements can introduce unforeseen variations that affect the quality of the model. This paper proposes a Genetic Programming based approach, where a transformation of the data from the second lab is evolved driven by classifier performance. A real-world problem, prostate cancer diagnosis, is presented as an example where the proposed approach was capable of repairing the fracture between the data of two different laboratories.
UR - http://www.scopus.com/inward/record.url?scp=79956294553&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79956294553&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17508-4_12
DO - 10.1007/978-3-642-17508-4_12
M3 - Conference contribution
AN - SCOPUS:79956294553
SN - 3642175074
SN - 9783642175077
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 185
EP - 197
BT - Learning Classifier Systems - 11th International Workshop, IWLCS 2008 and 12th International Workshop, IWLCS 2009, Revised Selected Papers
T2 - 11th International Workshop on Learning Classifier Systems, IWLCS 2008 and 12th International Workshop on Learning Classifier Systems, IWLCS 2009
Y2 - 9 July 2009 through 9 July 2009
ER -