An evolutionary approach to constructive induction for link discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Tim Weninger and William H. Hsu and Jing Xia and 
                 Waleed Aljandal",
  title =        "An evolutionary approach to constructive induction for
                 link discovery",
  booktitle =    "GECCO-2009 Late-Breaking Papers",
  year =         "2009",
  editor =       "Anna I. Esparcia and Ying-ping Chen and 
                 Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and 
                 Hans-Georg Beyer and Nikolaus Hansen and 
                 Steffen Finck and Raymond Ros and Darrell Whitley and 
                 Garnett Wilson and Simon Harding and W. B. Langdon and 
                 Man Leung Wong and Laurence D. Merkle and Frank W. Moore and 
                 Sevan G. Ficici and William Rand and Rick Riolo and 
                 Nawwaf Kharma and William R. Buckley and Julian Miller and 
                 Kenneth Stanley and Jaume Bacardit and Will Browne and 
                 Jan Drugowitsch and Nicola Beume and Mike Preuss and 
                 Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and 
                 Alexandru Floares and Aaron Baughman and 
                 Steven Gustafson and Maarten Keijzer and Arthur Kordon and 
                 Clare Bates Congdon and Laurence D. Merkle and 
                 Frank W. Moore",
  pages =        "2167--2172",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP,",
  DOI =          "doi:10.1145/1570256.1570295",
  abstract =     "This paper presents a genetic programming-based
                 symbolic regression approach to the construction of
                 relational features in link analysis applications.
                 Specifically, we consider the problems of predicting,
                 classifying and annotating friends relations in friends
                 networks, based upon features constructed from network
                 structure and user profile data. We first document a
                 data model for the blog service LiveJournal, and define
                 a set of machine learning problems such as predicting
                 existing links and estimating inter-pair distance.
                 Next, we explain how the problem of classifying a user
                 pair in a social network, as directly connected or not,
                 poses the problem of selecting and constructing
                 relevant features. We use genetic programming to
                 construct features, represented by multiple symbol
                 trees with base features as their leaves. In this
                 manner, the genetic program selects and constructs
                 features that may not have been originally considered,
                 but possess better predictive properties than the base
                 features. Finally, we present classification results
                 and compare these results with those of the control and
                 similar approaches.",
  notes =        "PhD University of Illinois 22 Aug 2013
        Discovering roles and
                 types from hierarchical information networks (not

                 Distributed on CD-ROM at GECCO-2009.

                 ACM Order Number 910092.",

Genetic Programming entries for Tim Weninger William H Hsu Jing Xia Waleed Aljandal