A Genetic Programming based Algorithm for Predicting Exchanges in Electronic Trade using Social Networks' Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4472

@Article{Poor:2017:IJACSA,
  author =       "Shokooh Sheikh Abooli Poor and 
                 Mohammad Ebrahim Shiri",
  title =        "A Genetic Programming based Algorithm for Predicting
                 Exchanges in Electronic Trade using Social Networks{'}
                 Data",
  journal =      "International Journal of Advanced Computer Science and
                 Applications (IJACSA)",
  year =         "2017",
  volume =       "8",
  number =       "5",
  keywords =     "genetic algorithms, genetic programming, electronic
                 business, social networks, prediction, machine
                 learning, facebook network",
  publisher =    "The Science and Information (SAI) Organization",
  bibsource =    "OAI-PMH server at thesai.org",
  description =  "International Journal of Advanced Computer Science and
                 Applications(IJACSA), 8(5), 2017",
  language =     "eng",
  oai =          "oai:thesai.org:10.14569/IJACSA.2017.080524",
  URL =          "http://thesai.org/Downloads/Volume8No5/Paper_24-A_Genetic_Programming_based_Algorithm.pdf",
  DOI =          "doi:10.14569/IJACSA.2017.080524",
  size =         "8 pages",
  abstract =     "Purpose of this paper is to use Facebook dataset for
                 predicting Exchanges in Electronic business. For this
                 purpose, first a dataset is collected from Facebook
                 users and this dataset is divided into two training and
                 test datasets. First, an advertisement post is sent for
                 training data users and feedback from each user is
                 recorded. Then, a learning machine is designed and
                 trained based on these feedbacks and users' profiles.
                 In order to design this learning machine, genetic
                 programming is used. Next, test dataset is used to test
                 the learning machine. The efficiency of the proposed
                 method is evaluated in terms of Precision, Accuracy,
                 Recall and F-Measure. Experiment results showed that
                 the proposed method outperforms basic algorithm (based
                 on J48) and random selection method in selecting
                 objective users for sending advertisements. The
                 proposed method has obtained Accuracy=74percent and
                 73percent earning ration in classifying users.",
}

Genetic Programming entries for Shokooh Sheikh Abooli Poor Mohammad Ebrahim Shiri Ahmad Abady

Citations