Feature Selection and Classification of High Dimensional Mass Spectrometry Data: A Genetic Programming Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Ahmed:2013:evobio,
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author = "Soha Ahmed and Mengjie Zhang and Lifeng Peng",
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title = "Feature Selection and Classification of High
Dimensional Mass Spectrometry Data: A Genetic
Programming Approach",
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booktitle = "11th European Conference on Evolutionary Computation,
Machine Learning and Data Mining in Bioinformatics,
{EvoBIO 2013}",
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year = "2013",
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editor = "Leonardo Vanneschi and William S. Bush and
Mario Giacobini",
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month = apr # " 3-5",
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series = "LNCS",
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volume = "7833",
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publisher = "Springer Verlag",
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organisation = "EvoStar",
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address = "Vienna, Austria",
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pages = "43--55",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-37188-2",
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DOI = "doi:10.1007/978-3-642-37189-9_5",
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abstract = "Biomarker discovery using mass spectrometry (MS) data
is very useful in disease detection and drug discovery.
The process of biomarker discovery in MS data must
start with feature selection as the number of features
in MS data is extremely large (e.g. thousands) while
the number of samples is comparatively small. In this
study, we propose the use of genetic programming (GP)
for automatic feature selection and classification of
MS data. This GP based approach works by using the
features selected by two feature selection metrics,
namely information gain (IG) and relief-f (REFS-F) in
the terminal set. The feature selection performance of
the proposed approach is examined and compared with IG
and REFS-F alone on five MS data sets with different
numbers of features and instances. Naive Bayes (NB),
support vector machines (SVMs) and J48 decision trees
(J48) are used in the experiments to evaluate the
classification accuracy of the selected features.
Meanwhile, GP is also used as a classification method
in the experiments and its performance is compared with
that of NB, SVMs and J48. The results show that GP as a
feature selection method can select a smaller number of
features with better classification performance than IG
and REFS-F using NB, SVMs and J48. In addition, GP as a
classification method also outperforms NB and J48 and
achieves comparable or slightly better performance than
SVMs on these data sets.",
- }
Genetic Programming entries for
Soha Ahmed
Mengjie Zhang
Lifeng Peng
Citations