On the Homogenization of Data from Two Laboratories Using Genetic Programming

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.

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