Automated Feature Design for Numeric Sequence Classification by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Dustin Y. Harvey and Michael D. Todd",
  title =        "Automated Feature Design for Numeric Sequence
                 Classification by Genetic Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2015",
  volume =       "19",
  number =       "4",
  pages =        "474--489",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Feature
                 design, machine learning, pattern recognition, sequence
                 classification, time series classification, time series
                 data mining.",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2014.2341451",
  size =         "16 pages",
  abstract =     "Pattern recognition methods rely on
                 maximum-information, minimum-dimension feature sets to
                 reliably perform classification and regression tasks.
                 Many methods exist to reduce feature set dimensionality
                 and construct improved features from an initial set;
                 however, there are few general approaches for the
                 design of features from numeric sequences. Any
                 information lost in preprocessing or feature
                 measurement cannot be recreated during pattern
                 recognition. General approaches are needed to extend
                 pattern recognition to include feature design and
                 selection for numeric sequences, such as time series,
                 within the learning process itself. This paper proposes
                 a novel genetic programming (GP) approach to automated
                 feature design called Autofead. In this method, a GP
                 variant evolves a population of candidate features
                 built from a library of sequence-handling functions.
                 Numerical optimization methods, included through a
                 hybrid approach, ensure that the fitness of candidate
                 algorithms is measured using optimal parameter values.
                 Autofead represents the first automated feature design
                 system for numeric sequences to leverage the power and
                 efficiency of both numerical optimisation and standard
                 pattern recognition algorithms. Potential applications
                 include the monitoring of electrocardiogram signals for
                 indications of heart failure, network traffic analysis
                 for intrusion detection systems, vibration measurement
                 for bearing condition determination in rotating
                 machinery, and credit card activity for fraud
  notes =        "Department of Structural Engineering, University of
                 California, San Diego

                 also known as \cite{6861439}",

Genetic Programming entries for Dustin Y Harvey Michael D Todd