- author = "Jose G. Moreno-Torres and Xavier Llora and David E. Goldberg and Rohit Bhargava",
- title = "Repairing fractures between data using genetic programming-based feature extraction: A case study in cancer diagnosis",
- journal = "Information Sciences",
- year = "2013",
- volume = "222",
- pages = "805--823",
- month = "10 " # feb,
- ISSN = "0020-0255",
-
DOI = "
doi:10.1016/j.ins.2010.09.018",
-
URL = "
http://www.sciencedirect.com/science/article/pii/S0020025510004585",
-
URL = "http://www.sciencedirect.com/science/article/B6V0C-515SRJV-1/2/ba19f0969d5d756d2abd12ac6f843d9f",
- keywords = "genetic algorithms, genetic programming, Feature extraction, Fractures between data, Biological data, Cancer diagnosis, Different laboratories",
- size = "19 pages",
-
abstract = "There is an underlying assumption on most model
building processes: given a learnt 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 behaviour.
The experimental analysis over benchmark problems together with a real-world problem on prostate cancer diagnosis show the good behavior of the proposed algorithm.",
- notes = "Including Special Section on New Trends in Ambient Intelligence and Bio-inspired Systems",
