Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Smart:2015:EAAI,
  author =       "Otis Smart and Lauren Burrell",
  title =        "Genetic programming and frequent itemset mining to
                 identify feature selection patterns of {iEEG} and
                 {fMRI} epilepsy data",
  journal =      "Engineering Applications of Artificial Intelligence",
  volume =       "39",
  pages =        "198--214",
  year =         "2015",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/j.engappai.2014.12.008",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0952197614003005",
  abstract =     "Pattern classification for intracranial
                 electroencephalogram (iEEG) and functional magnetic
                 resonance imaging (fMRI) signals has furthered epilepsy
                 research toward understanding the origin of epileptic
                 seizures and localising dysfunctional brain tissue for
                 treatment. Prior research has demonstrated that
                 implicitly selecting features with a genetic
                 programming (GP) algorithm more effectively determined
                 the proper features to discern biomarker and
                 non-biomarker interictal iEEG and fMRI activity than
                 conventional feature selection approaches. However for
                 each the iEEG and fMRI modalities, it is still
                 uncertain whether the stochastic properties of indirect
                 feature selection with a GP yield (a) consistent
                 results within a patient data set and (b) features that
                 are specific or universal across multiple patient data
                 sets. We examined the reproducibility of implicitly
                 selecting features to classify interictal activity
                 using a GP algorithm by performing several selection
                 trials and subsequent frequent itemset mining (FIM) for
                 separate iEEG and fMRI epilepsy patient data. We
                 observed within-subject consistency and across-subject
                 variability with some small similarity for selected
                 features, indicating a clear need for patient-specific
                 features and possible need for patient-specific feature
                 selection or/and classification. For the fMRI, using
                 nearest-neighbour classification and 30 GP generations,
                 we obtained over 60percent median sensitivity and over
                 60percent median selectivity. For the iEEG, using
                 nearest-neighbor classification and 30 GP generations,
                 we obtained over 65percent median sensitivity and over
                 65percent median selectivity except one patient.",
  keywords =     "genetic algorithms, genetic programming, Frequent
                 itemset mining, Feature selection, iEEG, fMRI,
                 Epilepsy",
}

Genetic Programming entries for Otis L Smart Lauren Burrell

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