Automated Feature Design for Time Series Classification by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Harvey:thesis,
  title =        "Automated Feature Design for Time Series
                 Classification by Genetic Programming",
  author =       "Dustin Yewell Harvey",
  year =         "2014",
  month =        jan # "~01",
  number =       "2",
  school =       "University of California, San Diego",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://escholarship.org/uc/item/1864t693.pdf",
  bibsource =    "OAI-PMH server at escholarship.org",
  coverage =     "1 PDF (1 online resource xvii, 122 pages)",
  identifier =   "qt1864t693",
  rights =       "public",
  URL =          "http://www.escholarship.org/uc/item/1864t693",
  broken =       "http://n2t.net/ark:/20775/bb7271474z",
  size =         "139 pages",
  abstract =     "Time series classification (TSC) methods discover and
                 exploit patterns in time series and other
                 one-dimensional signals. Although many accurate, robust
                 classifiers exist for multivariate feature sets,
                 general approaches are needed to extend machine
                 learning techniques to make use of signal inputs.
                 Numerous applications of TSC can be found in structural
                 engineering, especially in the areas of structural
                 health monitoring and non-destructive evaluation.
                 Additionally, the fields of process control, medicine,
                 data analytics, econometrics, image and facial
                 recognition, and robotics include TSC problems. This
                 dissertation details, demonstrates, and evaluates
                 Autofead, a novel approach to automated feature design
                 for TSC. In Autofead, a genetic programming variant
                 evolves a population of candidate solutions to optimise
                 performance for the TSC or time series regression task
                 based on training data. Solutions consist of features
                 built from a library of mathematical and digital signal
                 processing functions. Numerical optimisation methods,
                 included through a hybrid search approach, ensure that
                 the fitness of candidate feature algorithms is measured
                 using optimal parameter values. Experimental validation
                 and evaluation of the method is carried out on a wide
                 range of synthetic, laboratory, and real-world data
                 sets with direct comparison to conventional solutions
                 and state-of-the-art TSC methods. Autofead is shown to
                 be competitively accurate as well as producing highly
                 interpretable solutions that are desirable for data
                 mining and knowledge discovery tasks. Computational
                 cost of the search is relatively high in the learning
                 stage to design solutions; however, the computational
                 expense for classifying new time series is very low
                 making Autofead solutions suitable for embedded and
                 real-time systems. Autofead represents a powerful,
                 general tool for TSC and time series data mining
                 researchers as well as industry practitioners.
                 Potential applications are numerous including 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 detection. In
                 addition to the development of the overall method, this
                 dissertation provides contributions in the areas of
                 evolutionary computation, numerical optimisation,
                 digital signal processing, and uncertainty analysis for
                 evaluating solution robustness",
}

Genetic Programming entries for Dustin Y Harvey

Citations