Repairing fractures between data using genetic programming-based feature extraction: A case study in cancer diagnosis

  • Authors:

  • Jose G. Moreno-Torres;Xavier Llorí;David E. Goldberg;Rohit Bhargava

  • Affiliations:

  • Department of Computer Science and Artificial Intelligence, Universidad de Granada, 18071 Granada, Spain;National Center for Supercomputing Applications (NCSA), University of Illinois at Urbana-Champaign 1205 W. Clark Street, Urbana, Illinois, USA;Illinois Genetic Algorithms Laboratory (IlliGAL) University of Illinois at Urbana-Champaign 104 S. Mathews Ave, Urbana, Illinois, USA;Department of Bioengineering, University of Illinois at Urbana-Champaign 405 N. Mathews Ave, Urbana, Illinois, USA

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

There is an underlying assumption on most model building processes: given a learned classifier, it should be usable to explain unseen data from the same given problem. Despite this seemingly reasonable assumption, when dealing with biological data it tends to fail; where classifiers built out of data generated using the same protocols in two different laboratories can lead to two different, non-interchangeable, classifiers. There are usually too many uncontrollable variables in the process of generating data in the lab and biological variations, and small differences can lead to very different data distributions, with a fracture between data. This paper presents a genetics-based machine learning approach that performs feature extraction on data from a lab to help increase the classification performance of an existing classifier that was built using the data from a different laboratory which uses the same protocols, while learning about the shape of the fractures between data that motivated the bad behavior. The experimental analysis over benchmark problems together with a real-world problem on prostate cancer diagnosis show the good behavior of the proposed algorithm.