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

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Boosting method via the sparse learner approach for high-dimensional gene expression data

https://ir.soken.ac.jp/records/2169

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Item type

学位論文 / Thesis or Dissertation(1)

公開日

2011-06-03

タイトル

Boosting method via the sparse learner approach for high-dimensional gene expression data

タイトル

Boosting method via the sparse learner approach for high-dimensional gene expression data

言語

eng

資源タイプ

資源タイプ識別子

http://purl.org/coar/resource_type/c_46ec

資源タイプ

thesis

著者名

プリチャード, 真理

フリガナ

プリチャード, マリ

著者

PRITCHARD, Mari

学位授与機関

学位授与機関名

総合研究大学院大学

学位名

博士（統計科学）

学位記番号

内容記述タイプ

Other

内容記述

総研大甲第1383号

研究科

値

複合科学研究科

専攻

値

15 統計科学専攻

学位授与年月日

2010-09-30

学位授与年度

値

2010

要旨

内容記述タイプ

Other

内容記述

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.

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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

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