On the homogenization of data from two laboratories using Genetic Programming

Jose G. Moreno-Torres, Xavier Llorà, David E. Goldberg, Rohit Bhargava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationLearning Classifier Systems - 11th International Workshop, IWLCS 2008 and 12th International Workshop, IWLCS 2009, Revised Selected Papers
Pages185-197
Number of pages13
DOIs
StatePublished - Dec 1 2010
Event11th International Workshop on Learning Classifier Systems, IWLCS 2008 and 12th International Workshop on Learning Classifier Systems, IWLCS 2009 - Montreal, QC, Canada
Duration: Jul 9 2009Jul 9 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6471 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Workshop on Learning Classifier Systems, IWLCS 2008 and 12th International Workshop on Learning Classifier Systems, IWLCS 2009
CountryCanada
CityMontreal, QC
Period7/9/097/9/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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