Feature selection and classification of pro-TOF data based on soft information

Lin Zhang, Jian Qiu Zhang, Xiao Bo Zhou, Hong Hui Wang, Yu Fei Huang, Hui Liu, Stephen T. Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we introduce a feature selection and classification method for prOTOF Mass Spectrometry (MS) data profiles of diseased and healthy patients. The method is based on a special statistical measure, which quantifies the probability of the existence of peptidepeaks. A special ranking score that is based on the statistical measure is used for selecting features that can best distinguish diseased and healthy data profiles. Based on the selected features, we applied a variety of classification algorithms and the results are compared with that of a method which selects features only based on peak heights. The results show a significant improvement in classification error rate with our proposed method.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Pages4018-4023
Number of pages6
Volume7
DOIs
StatePublished - Dec 23 2008
Event7th International Conference on Machine Learning and Cybernetics, ICMLC - Kunming, China
Duration: Jul 12 2008Jul 15 2008

Other

Other7th International Conference on Machine Learning and Cybernetics, ICMLC
CountryChina
CityKunming
Period7/12/087/15/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Control and Systems Engineering

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