Differentiating Between Individual Class Performance in Genetic Programming Fitness for Classification with Unbalanced Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Bhowan:2009:cec,
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author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang",
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title = "Differentiating Between Individual Class Performance
in Genetic Programming Fitness for Classification with
Unbalanced Data",
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booktitle = "2009 IEEE Congress on Evolutionary Computation",
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year = "2009",
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editor = "Andy Tyrrell",
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pages = "2802--2809",
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address = "Trondheim, Norway",
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month = "18-21 " # may,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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isbn13 = "978-1-4244-2959-2",
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file = "P289.pdf",
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DOI = "doi:10.1109/CEC.2009.4983294",
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abstract = "This paper investigates improvements to the fitness
function in Genetic Programming to better solve binary
classification problems with unbalanced data. Data sets
are unbalanced when there is a majority of examples for
one particular class over the other class(es). We show
that using overall classification accuracy as the
fitness function evolves classifiers with a performance
bias toward the majority class at the expense of
minority class performance. We develop four new fitness
functions which consider the accuracy of majority and
minority class separately to address this learning
bias. Results using these fitness functions show that
good accuracy for both the minority and majority
classes can be achieved from evolved classifiers while
keeping overall performance high and balanced across
the two classes.",
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keywords = "genetic algorithms, genetic programming",
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notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
- }
Genetic Programming entries for
Urvesh Bhowan
Mark Johnston
Mengjie Zhang
Citations