@misc{oai:ir.soken.ac.jp:00002169,
 author = {プリチャード, 真理 and プリチャード, マリ and PRITCHARD, Mari},
 month = {2016-02-17, 2016-02-17},
 note = {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},
 title = {Boosting method via the sparse learner approach for high-dimensional gene expression data},
 year = {}
}