Evolutionary computing for knowledge discovery in medical diagnosis

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Tan:2003:AIM,

• author = "K. C. Tan and Q. Yu and C. M. Heng and T. H. Lee",
• title = "Evolutionary computing for knowledge discovery in medical diagnosis",
• journal = "Artificial Intelligence in Medicine",
• year = "2003",
• volume = "27",
• pages = "129--154",
• number = "2",
• keywords = "genetic algorithms, genetic programming, Medical diagnosis, Knowledge discovery, Data mining, Evolutionary computing",
• owner = "wlangdon",
• URL = "http://www.sciencedirect.com/science/article/B6T4K-47RRWS9-2/2/5c8dfaf6e49d194b0c8ed6e2fd1b5117",
• ISSN = "0933-3657",
• DOI = "doi:10.1016/S0933-3657(03)00002-2",
• abstract = "One of the major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis data. a two-phase hybrid evolutionary classification technique is proposed to extract classification rules that can be used in clinical practice for better understanding and prevention of unwanted medical events. In the first phase, a hybrid evolutionary algorithm (EA) is used to confine the search space by evolving a pool of good candidate rules, e.g. genetic programming (GP) is applied to evolve nominal attributes for free structured rules and genetic algorithm (GA) is used to optimise the numeric attributes for concise classification rules without the need of discretisation. These candidate rules are then used in the second phase to optimize the order and number of rules in the evolution for forming accurate and comprehensible rule sets. The proposed evolutionary classifier (EvoC) is validated upon hepatitis and breast cancer datasets obtained from the UCI machine-learning repository. Simulation results show that the evolutionary classifier produces comprehensible rules and good classification accuracy for the medical datasets. Results obtained from t-tests further justify its robustness and invariance to random partition of datasets.",
• notes = "PMID: 12636976",

}

Genetic Programming entries for Kay Chen Tan Qi Yu C M Heng Tong Heng Lee

Citations