Prediction of Successful Participation in a Lifestyle Activity Program using Data Mining Techniques

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

@InProceedings{Pijl:2009:BNAIC,
  author =       "Marten Pijl and Joyca Lacroix and Steffen Pauws and 
                 Annelies Goris",
  title =        "Prediction of Successful Participation in a Lifestyle
                 Activity Program using Data Mining Techniques",
  booktitle =    "The 21st Benelux Conference on Artificial Intelligence
                 (BNAIC)",
  year =         "2009",
  editor =       "Toon Calders and Karl Tuyls and Mykola Pechenizkiy",
  address =      "Eindhoven",
  month =        "29-30 " # oct,
  organisation = "BNVKI",
  note =         "Industry Track",
  keywords =     "genetic algorithms, genetic programming",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.380.4042",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.380.4042",
  URL =          "http://wwwis.win.tue.nl/bnaic2009/papers/bnaic2009_paper_114.pdf",
  size =         "8 pages",
  abstract =     "The growing number of people worldwide living a
                 sedentary life has led to increased efforts into the
                 development of effective physical activity intervention
                 programs. However, many participants of such programs
                 fail to complete the program, and as a result do not
                 attain the desired increase in physical activity. By
                 detecting participants who are at risk of dropping out
                 in advance, it may be possible to intervene and prevent
                 the participant from dropping out. The work in this
                 paper discusses the classification of such
                 participants, using an activity database containing
                 participant characteristics and activity data gathered
                 through an accelerometer worn by the participants.
                 Using genetic programming techniques, database
                 variables (called markers) are combined to form new
                 sets of markers. The predictive power of these new
                 markers are compared to the markers present in the
                 database, based on classification using principal
                 component analysis and a k-nearest neighbour
                 classifier. Results show that without the use of
                 genetic programming to combine markers, classification
                 class accuracy is approximately 64percent. By combining
                 markers through genetic programming, classification
                 class accuracy increases to 72percent, which equates to
                 a reduction in the number of errors of approximately
                 23percent",
  notes =        "Philips",
}

Genetic Programming entries for Marten Pijl Joyca Lacroix Steffen Pauws Annelies Goris

Citations