An Improved Knowledge-Acquisition Strategy Based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@Article{journals/cas/KuoHC08,

• title = "An Improved Knowledge-Acquisition Strategy Based on Genetic Programming",
• author = "Chan-Sheng Kuo and Tzung-Pei Hong and Chuen-Lung Chen",
• journal = "Cybernetics and Systems",
• year = "2008",
• number = "7",
• volume = "39",
• bibdate = "2008-12-12",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cas/cas39.html#KuoHC08",
• pages = "672--685",
• DOI = "doi:10.1080/01969720802257881",
• month = oct,
• keywords = "genetic algorithms, genetic programming",
• size = "15 pages",
• abstract = "Knowledge acquisition can deal with the task of extracting desirable or useful knowledge from data sets for a practical application. In this paper, we have modified our previous gp-based learning strategy to search for an appropriate classification tree. The proposed approach consists of three phases: knowledge creation, knowledge evolution, and knowledge output. In the creation phase, a set of classification trees are randomly generated to form an initial knowledge population. In the evolution phase, the genetic programming technique is used to generate a good classification tree. In the output phase, the final derived classification tree is transferred as a rule set, then outputted to the knowledge base to facilitate the inference of new data. One new genetic operator, separation, is designed in this proposed approach to remove contradiction, thus producing more accurate classification rules. Experimental results from the diagnosis of breast cancers also show the feasibility of the proposed algorithm.",

}

Genetic Programming entries for Chan-Sheng Kuo Tzung-Pei Hong Samuel Chuen-Lung Chen

Citations