Multi-Instance Learning with MultiObjective Genetic Programming

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

@InCollection{reference/dataware/Zafra09,
  author =       "Amelia Zafra",
  title =        "Multi-Instance Learning with MultiObjective Genetic
                 Programming",
  booktitle =    "Encyclopedia of Data Warehousing and Mining",
  publisher =    "IGI Global",
  year =         "2009",
  editor =       "John Wang",
  chapter =      "212",
  pages =        "1372--1379",
  edition =      "2",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "9781605660103",
  URL =          "http://www.igi-global.com/bookstore/titledetails.aspx?titleid=346&detailstype=chapters",
  DOI =          "doi:10.4018/978-1-60566-010-3.ch212",
  DOI =          "doi:10.4018/978-1-60566-010-3",
  abstract =     "The multiple-instance problem is a difficult machine
                 learning problem that appears in cases where knowledge
                 about training examples is incomplete. In this problem,
                 the teacher labels examples that are sets (also called
                 bags) of instances. The teacher does not label whether
                 an individual instance in a bag is positive or
                 negative. The learning algorithm needs to generate a
                 classifier that will correctly classify unseen examples
                 (i.e., bags of instances). This learning framework is
                 receiving growing attention in the machine learning
                 community and since it was introduced by Dietterich,
                 Lathrop, Lozano-Perez (1997), a wide range of tasks
                 have been formulated as multi-instance problems. Among
                 these tasks, we can cite content-based image retrieval
                 (Chen, Bi, & Wang, 2006) and annotation (Qi and Han,
                 2007), text categorisation (Andrews, Tsochantaridis, &
                 Hofmann, 2002), web index page recommendation (Zhou,
                 Jiang, & Li, 2005; Xue, Han, Jiang, & Zhou, 2007) and
                 drug activity prediction (Dietterich et al., 1997; Zhou
                 & Zhang, 2007). In this chapter we introduce MOG3P-MI,
                 a multiobjective grammar guided genetic programming
                 algorithm to handle multi-instance problems. In this
                 algorithm, based on SPEA2, individuals represent
                 classification rules which make it possible to
                 determine if a bag is positive or negative. The quality
                 of each individual is evaluated according to two
                 quality indexes: sensitivity and specificity. Both
                 these measures have been adapted to MIL circumstances.
                 Computational experiments show that the MOG3P-MI is a
                 robust algorithm for classification in different
                 domains where achieves competitive results and obtain
                 classifiers which contain simple rules which add
                 comprehensibility and simplicity in the knowledge
                 discovery process, being suitable method for solving
                 MIL problems (Zafra & Ventura, 2007).",
  notes =        "4 Volumes. University of Cordoba, Spain",
  bibdate =      "2011-01-18",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/reference/dataware/dataware2009.html#Zafra09",
}

Genetic Programming entries for Amelia Zafra Gomez

Citations