Using Ant Programming Guided by Grammar for Building Rule-Based Classifiers
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Olmo:2011:smc,
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author = "Juan Luis Olmo and Jose Raul Romero and
Sebastian Ventura",
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title = "Using Ant Programming Guided by Grammar for Building
Rule-Based Classifiers",
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journal = "IEEE Transactions on Systems, Man, and Cybernetics,
Part B: Cybernetics",
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year = "2011",
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volume = "41",
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number = "6",
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pages = "1585--1599",
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month = dec,
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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",
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ISSN = "1083-4419",
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DOI = "doi:10.1109/TSMCB.2011.2157681",
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size = "15 pages",
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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