GroupAdaBoost: Accurate Prediction and Selection of Important Genes

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    • Takenouchi Takashi
    • Graduate School of Information Science, Nara Institute of Science and Technology
    • Eguchi Shinto
    • Institute of Statistical Mathematics, Japan and Department of Statistical Science, Graduate University of Advanced Studies


In this paper, we propose GroupAdaBoost which is a variant of AdaBoost for statistical pattern recognition. The objective of the proposed algorithm is to solve the “ p » n ”problem arisen in bioinformatics. In a microarray experiment, gene expressions are observed to extract any specific pattern of gene expressions related to a disease status. Typically, p is the number of investigated genes and n is number of individuals. The ordinary method for predicting the genetic causes of diseases is apt to over-learn from any particular training dataset because of the“ p » n ” problem. We observed that GroupAdaBoost gave a robust performance for cases of the excess number p of genes. In several real datasets which are publicly available from web-pages, we compared the analysis of results among the proposed method and others, and a small scale of simulation study to confirm the validity of the proposed method. Additionally the proposed method effectively worked for the identification of important genes.


  • IPSJ Digital Courier

    IPSJ Digital Courier (3), 145-152, 2007

    Information Processing Society of Japan

Cited by:  1


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