Preliminary Assessment of an SFFS Method for Sub-Group Feature Identification in Heterogeneous Data Sets

Ronald C. Anderson, Mary C. Baker

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Many biomedical pattern recognition problems involve disorders or conditions that present with different symptoms or features, resulting in a data set that is not homogeneous across an affected population. Examples of such data sets may include those describing autism spectrum disorders and mild cognitive impairment. In this paper, we describe preliminary analyses with synthetic data sets that simulate feature synergies inherent in many of these problems. A sequential forward floating search (SFFS) algorithm is then used to select relevant features for classification purposes. Our results suggest the SFFS method of feature selection may be an ideal technique when working with such data sets.

Original languageEnglish
Title of host publicationProceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
EditorsBridget Kane, Jaakko Hollmen, Carolyn McGregor, Paolo Soda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages182-187
Number of pages6
ISBN (Electronic)9781538660607
DOIs
StatePublished - Jul 20 2018
Event31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018 - Karlstad, Sweden
Duration: Jun 18 2018Jun 21 2018

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2018-June
ISSN (Print)1063-7125

Conference

Conference31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
CountrySweden
CityKarlstad
Period06/18/1806/21/18

Keywords

  • feature-selection
  • heterogeneous-data
  • subgroups

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