Using Ant Programming Guided by Grammar for Building Rule-Based Classifiers

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Olmo:2011:smc,
  author =       "Juan Luis Olmo and Jose Raul Romero and 
                 Sebastian Ventura",
  title =        "Using Ant Programming Guided by Grammar for Building
                 Rule-Based Classifiers",
  journal =      "IEEE Transactions on Systems, Man, and Cybernetics,
                 Part B: Cybernetics",
  year =         "2011",
  volume =       "41",
  number =       "6",
  pages =        "1585--1599",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, context-free
                 grammars, data mining, optimisation, pattern
                 classification, ant-based algorithm, classification
                 algorithm, classification rules mining, context-free
                 grammar, expert domain decision, grammar based ant
                 programming, rule-based classifiers, Algorithm design
                 and analysis, Ant colony optimisation, Automatic
                 programming, Classification algorithms, Data mining,
                 Grammar, Ant colony optimization (ACO), ant programming
                 (AP), classification, data mining (DM), grammar-based
                 automatic programming",
  ISSN =         "1083-4419",
  DOI =          "doi:10.1109/TSMCB.2011.2157681",
  size =         "15 pages",
  abstract =     "The extraction of comprehensible knowledge is one of
                 the major challenges in many domains. In this paper, an
                 ant programming (AP) framework, which is capable of
                 mining classification rules easily comprehensible by
                 humans, and, therefore, capable of supporting
                 expert-domain decisions, is presented. The algorithm
                 proposed, called grammar based ant programming (GBAP),
                 is the first AP algorithm developed for the extraction
                 of classification rules, and it is guided by a
                 context-free grammar that ensures the creation of new
                 valid individuals. To compute the transition
                 probability of each available movement, this new model
                 introduces the use of two complementary heuristic
                 functions, instead of just one, as typical ant-based
                 algorithms do. The selection of a consequent for each
                 rule mined and the selection of the rules that make up
                 the classifier are based on the use of a niching
                 approach. The performance of GBAP is compared against
                 other classification techniques on 18 varied data sets.
                 Experimental results show that our approach produces
                 comprehensible rules and competitive or better accuracy
                 values than those achieved by the other classification
                 algorithms compared with it.",
  notes =        "Also known as \cite{5936743}",
}

Genetic Programming entries for Juan Luis Olmo Jose Raul Romero Salguero Sebastian Ventura

Citations