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.
|Title of host publication
|Proceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
|Bridget Kane, Jaakko Hollmen, Carolyn McGregor, Paolo Soda
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Jul 20 2018
|31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018 - Karlstad, Sweden
Duration: Jun 18 2018 → Jun 21 2018
|Proceedings - IEEE Symposium on Computer-Based Medical Systems
|31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
|06/18/18 → 06/21/18