A New GP-Based Wrapper Feature Construction Approach to Classification and Biomarker Identification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Ahmed:2014:CEC,
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title = "A New {GP}-Based Wrapper Feature Construction Approach
to Classification and Biomarker Identification",
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author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng",
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pages = "2756--2763",
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booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary
Computation",
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year = "2014",
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month = "6-11 " # jul,
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editor = "Carlos A. {Coello Coello}",
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address = "Beijing, China",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, Evolutionary
programming, Biometrics, bioinformatics and biomedical
applications",
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DOI = "doi:10.1109/CEC.2014.6900317",
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abstract = "Mass spectrometry (MS) is a technology used for
identification and quantification of proteins and
metabolites. It helps in the discovery of proteomic or
metabolomic biomarkers, which aid in diseases detection
and drug discovery. The detection of biomarkers is
performed through the classification of patients from
healthy samples. The mass spectrometer produces high
dimensional data where most of the features are
irrelevant for classification. Therefore, feature
reduction is needed before the classification of MS
data can be done effectively. Feature construction can
provide a means of dimensionality reduction and aims at
improving the classification performance. In this
paper, genetic programming (GP) is used for
construction of multiple features. Two methods are
proposed for this objective. The proposed methods work
by wrapping a Random Forest (RF) classifier to GP to
ensure the quality of the constructed features.
Meanwhile, five other classifiers in addition to RF are
used to test the impact of the constructed features on
the performance of these classifiers. The results show
that the proposed GP methods improved the performance
of classification over using the original set of
features in five MS data sets.",
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notes = "WCCI2014",
- }
Genetic Programming entries for
Soha Ahmed
Mengjie Zhang
Lifeng Peng
Citations