Peptide Classification with Genetic Programming Ensemble of Generalised Indicator Models

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{conf/imecs/Yang07,

• author = "Zheng Rong Yang",
• title = "Peptide Classification with Genetic Programming Ensemble of Generalised Indicator Models",
• booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2007",
• year = "2007",
• editor = "Sio Iong Ao and Oscar Castillo and Craig Douglas and David Dagan Feng and Jeong-A. Lee",
• series = "Lecture Notes in Engineering and Computer Science",
• pages = "319--324",
• address = "Hong Kong",
• month = mar # " 21-23",
• publisher = "Newswood Limited",
• note = "Certificate of Merit",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-988-98671-4-0; 978-988-98671-7-1",
• bibdate = "2008-01-24",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/imecs/imecs2007.html#Yang07",
• abstract = "The generalised indicator model (GIM) has been developed for peptide classification with success. However, the performance of GIM varies with the mutation matrix which is used to measure the similarity between peptides. This work investigates three methods for building meta-classifiers based on GIMs which are treated as base classifiers constructed using different mutation matrices. The three methods are linear combination, neural network combination and genetic programming. The simulation shows that the genetic programming method performs the best in two aspects. First, it is able to identify the most important base classifiers for building a meta-classifier without any a priori knowledge. Second, a metaclassfier delivered is a mathematical equation being capable of interpretation.",
• notes = "http://www.iaeng.org/IMECS2007/schedule/schedule_ICB.html http://www.iaeng.org/IMECS2007/Best_paper_awards.html",

}

Genetic Programming entries for Zheng Rong Yang

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