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Boosting method via the sparse learner approach for high-dimensional gene expression data Boosting method via the sparse learner approach for high-dimensional gene expression data プリチャード, 真理 プリチャード, マリ PRITCHARD, Mari Gene expression analysis is commonly used to analyze millions of gene ex- pression data points.　Challenging in this process has been the development of appropriate statistical methods for high-dimensional data.　We propose Sparse Learner Boosting for gene expression data analysis.　Boosting is performed to minimize the loss function, although this process can cause overfitting when a large number of variables are present.　Ordinary boosting utilizes all of the potential weak learners in a given data set and constructs a decision rule.　The fundamental idea of Sparse Learner Boosting is to reduce the complexity of the decision rule by using fewer weak learners than is usually required.　This reduction prevents overfitting and improves performance during classification. Numerical studies support this modification for high-dimensional data, such as that obtained from gene expression analysis. We show that the proposed modification improves the performance of ordinary boosting methods. We also review another problem in high-dimensional data.　Sparser solutions are desirable from the view point of simple classification modeling and ease of interpretation however there is no unique sparse solution in any single classifi- cation problem.　The possible combination of gene sets out of millions of gene expression data is huge.　We show the existence of multiple optimum gene sets and consider the possible solutions. application/pdf 総研大甲第1383号 eng thesis https://ir.soken.ac.jp/records/2169 博士（統計科学） 2010-09-30 総合研究大学院大学 https://ir.soken.ac.jp/record/2169/files/甲1383_要旨.pdf application/pdf 169.2 kB 2016-02-17 https://ir.soken.ac.jp/record/2169/files/甲1383_本文.pdf application/pdf 996.0 kB 2016-02-17