Two layered Genetic Programming for mixed-attribute data classification

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

@Article{Jabeen2012416,
  author =       "Hajira Jabeen and Abdul Rauf Baig",
  title =        "Two layered Genetic Programming for mixed-attribute
                 data classification",
  journal =      "Applied Soft Computing",
  volume =       "12",
  number =       "1",
  pages =        "416--422",
  year =         "2012",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2011.08.029",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494611003127",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, Mixed attribute data, Mixed type data
                 classification, Classifier",
  abstract =     "The important problem of data classification spans
                 numerous real life applications. The classification
                 problem has been tackled by using Genetic Programming
                 in many successful ways. Most approaches focus on
                 classification of only one type of data. However, most
                 of the real-world data contain a mixture of categorical
                 and continuous attributes. In this paper, we present an
                 approach to classify mixed attribute data using Two
                 Layered Genetic Programming (L2GP). The presented
                 approach does not transform data into any other type
                 and combines the properties of arithmetic expressions
                 (using numerical data) and logical expressions (using
                 categorical data). The outer layer contains logical
                 functions and some nodes. These nodes contain the inner
                 layer and are either logical or arithmetic expressions.
                 Logical expressions give their Boolean output to the
                 outer tree. The arithmetic expressions give a real
                 value as their output. Positive real value is
                 considered true and a negative value is considered
                 false. These outputs of inner layers are used to
                 evaluate the outer layer which determines the
                 classification decision. The proposed classification
                 technique has been applied on various heterogeneous
                 data classification problems and found successful.",
}

Genetic Programming entries for Hajira Jabeen Abdul Rauf Baig

Citations