A Study of Decision Tree Induction for Data Stream Mining Using Boosting Genetic Programming Classifier

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

@InProceedings{conf/semcco/KumarMSP11,
  author =       "Dirisala J. {Nagendra Kumar} and J. V. R. Murthy and 
                 Suresh Chandra Satapathy and 
                 S. V. V. S. R. Kumar Pullela",
  title =        "A Study of Decision Tree Induction for Data Stream
                 Mining Using Boosting Genetic Programming Classifier",
  booktitle =    "Proceedings of the Second International Conference on
                 Swarm, Evolutionary, and Memetic Computing (SEMCCO
                 2011) Part {I}",
  year =         "2011",
  editor =       "Bijaya K. Panigrahi and 
                 Ponnuthurai Nagaratnam Suganthan and Swagatam Das and 
                 Suresh Chandra Satapathy",
  volume =       "7076",
  series =       "Lecture Notes in Computer Science",
  pages =        "315--322",
  address =      "Visakhapatnam, Andhra Pradesh, India",
  month =        dec # " 19-21",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-27171-7",
  DOI =          "doi:10.1007/978-3-642-27172-4_39",
  size =         "8 pages",
  abstract =     "Genetic Programming is an evolutionary soft computing
                 approach. Data streams are the order of the day input
                 mechanisms. Here is a study of GP Classifier on Data
                 Streams. GP classification performance is compared to
                 that of other state-of-the-art data mining and stream
                 classification approaches. Boosting is a machine
                 learning meta-algorithm for performing supervised
                 learning. A weak learner is defined to be a classifier
                 which is only slightly correlated with the true
                 classification (it can label examples better than
                 random guessing). In contrast, a strong learner is a
                 classifier that is arbitrarily well-correlated with the
                 true classification. Boosting combines a set of weak
                 learners to create a strong learner. It is observed
                 that the Boosting GP approach is beating Boosting Naive
                 Bayes classification. Hence it is found that GP is a
                 competent algorithm for Data Stream classification.",
  affiliation =  "BVRICE, Bhimavaram, India",
  bibdate =      "2011-12-14",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/semcco/semcco2011-1.html#KumarMSP11",
}

Genetic Programming entries for Dirisala J Nagendra Kumar J V R Murthy Suresh Chandra Satapathy S V V S R Kumar Pullela

Citations