Cancer Prediction Using Diversity-Based Ensemble Genetic Programming

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

@InProceedings{conf/mdai/HongC05,

• title = "Cancer Prediction Using Diversity-Based Ensemble Genetic Programming",
• author = "Jin-Hyuk Hong and Sung-Bae Cho",
• year = "2005",
• pages = "294--304",
• editor = "Vicenc Torra and Yasuo Narukawa and Sadaaki Miyamoto",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "3558",
• booktitle = "Modeling Decisions for Artificial Intelligence, Second International Conference, MDAI 2005, Proceedings",
• address = "Tsukuba, Japan",
• month = jul # " 25-27",
• bibdate = "2005-07-18",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/mdai/mdai2005.html#HongC05",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-27871-0",
• DOI = "doi:10.1007/11526018_29",
• abstract = "Combining a set of classifiers has often been exploited to improve the classification performance. Accurate as well as diverse base classifiers are prerequisite to construct a good ensemble classifier. Therefore, estimating diversity among classifiers has been widely investigated. This paper presents an ensemble approach that combines a set of diverse rules obtained by genetic programming. Genetic programming generates interpretable classification rules, and diversity among them is directly estimated. Finally, several diverse rules are combined by a fusion method to generate a final decision. The proposed method has been applied to cancer classification using gene expression profiles, which is one of the important issues in bioinformatics. Experiments on several popular cancer datasets have demonstrated the usability of the method. High performance of the proposed method has been obtained, and the accuracy has increased by diversity among the base classification rules.",

}

Genetic Programming entries for Jin-Hyuk Hong Sung Bae Cho

Citations