Multiple feature construction for effective biomarker identification and classification using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Ahmed:2014:GECCOa,
  author =       "Soha Ahmed and Mengjie Zhang and Lifeng Peng and 
                 Bing Xue",
  title =        "Multiple feature construction for effective biomarker
                 identification and classification using genetic
                 programming",
  booktitle =    "GECCO '14: Proceedings of the 2014 conference on
                 Genetic and evolutionary computation",
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2662-9",
  pages =        "249--256",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2576768.2598292",
  DOI =          "doi:10.1145/2576768.2598292",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Biomarker identification, i.e., detecting the features
                 that indicate differences between two or more classes,
                 is an important task in omics sciences. Mass
                 spectrometry (MS) provide a high throughput analysis of
                 proteomic and metabolomic data. The number of features
                 of the MS data sets far exceeds the number of samples,
                 making biomarker identification extremely difficult.
                 Feature construction can provide a means for solving
                 this problem by transforming the original features to a
                 smaller number of high-level features. This paper
                 investigates the construction of multiple features
                 using genetic programming (GP) for biomarker
                 identification and classification of mass spectrometry
                 data. In this paper, multiple features are constructed
                 using GP by adopting an embedded approach in which
                 Fisher criterion and p-values are used to measure the
                 discriminating information between the classes. This
                 produces nonlinear high-level features from the
                 low-level features for both binary and multi-class mass
                 spectrometry data sets. Meanwhile, seven different
                 classifiers are used to test the effectiveness of the
                 constructed features. The proposed GP method is tested
                 on eight different mass spectrometry data sets. The
                 results show that the high-level features constructed
                 by the GP method are effective in improving the
                 classification performance in most cases over the
                 original set of features and the low-level selected
                 features. In addition, the new method shows superior
                 performance in terms of biomarker detection rate.",
  notes =        "Also known as \cite{2598292} GECCO-2014 A joint
                 meeting of the twenty third international conference on
                 genetic algorithms (ICGA-2014) and the nineteenth
                 annual genetic programming conference (GP-2014)",
}

Genetic Programming entries for Soha Ahmed Mengjie Zhang Lifeng Peng Bing Xue

Citations