Ensemble Genetic Programming for Classifying Gene Expression Data

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Hong:2004:aspgp,
  author =       "Jin-Hyuk Hong and Sung-Bae Cho",
  title =        "Ensemble Genetic Programming for Classifying Gene
                 Expression Data",
  booktitle =    "Proceedings of The Second Asian-Pacific Workshop on
                 Genetic Programming",
  year =         "2004",
  editor =       "R I Mckay and Sung-Bae Cho",
  address =      "Cairns, Australia",
  month =        "6-7 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://sclab.yonsei.ac.kr/publications/Papers/IC/ASPGP04_Final.pdf",
  size =         "12 pages",
  abstract =     "Ensemble is a representative technique for improving
                 classification performance by combining a set of
                 classifiers. It is required to maintain the diversity
                 among base classifiers for effective ensemble.
                 Conventional ensemble approaches construct various
                 classifiers by estimating the similarity on the output
                 patterns of them, and combine them with several fusion
                 methods. Since they measure the similarity indirectly,
                 it is restricted to evaluate the precise diversity
                 among base classifiers. In this paper, we propose an
                 ensemble method that estimates the similarity between
                 classification rules by matching in
                 representation-level. A set of comprehensive and
                 precise rules is obtained by genetic programming. After
                 evaluating the diversity, a fusion method makes the
                 final decision with a subset of diverse classification
                 rules. The proposed method is applied to cancer
                 classification using gene expression profiles, which
                 requires high accuracy and reliability. Especially, the
                 experiments on popular cancer datasets have
                 demonstrated the usefulness of the proposed method.",
  notes =        "http://sc.snu.ac.kr/~aspgp/aspgp04/programme.html",
}

Genetic Programming entries for Jin-Hyuk Hong Sung Bae Cho

Citations