{"_buckets": {"deposit": "cca09a81-c2a3-4793-9f97-8eb6d7515d9d"}, "_deposit": {"created_by": 21, "id": "2169", "owners": [21], "pid": {"revision_id": 0, "type": "depid", "value": "2169"}, "status": "published"}, "_oai": {"id": "oai:ir.soken.ac.jp:00002169", "sets": ["17"]}, "author_link": ["0", "0", "0"], "item_1_biblio_info_21": {"attribute_name": "書誌情報（ソート用）", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2010-09-30", "bibliographicIssueDateType": "Issued"}, "bibliographic_titles": [{}]}]}, "item_1_creator_2": {"attribute_name": "著者名", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "プリチャード, 真理"}], "nameIdentifiers": [{"nameIdentifier": "0", "nameIdentifierScheme": "WEKO"}]}]}, "item_1_creator_3": {"attribute_name": "フリガナ", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "プリチャード, マリ"}], "nameIdentifiers": [{"nameIdentifier": "0", "nameIdentifierScheme": "WEKO"}]}]}, "item_1_date_granted_11": {"attribute_name": "学位授与年月日", "attribute_value_mlt": [{"subitem_dategranted": "2010-09-30"}]}, "item_1_degree_grantor_5": {"attribute_name": "学位授与機関", "attribute_value_mlt": [{"subitem_degreegrantor": [{"subitem_degreegrantor_name": "総合研究大学院大学"}]}]}, "item_1_degree_name_6": {"attribute_name": "学位名", "attribute_value_mlt": [{"subitem_degreename": "博士（統計科学）"}]}, "item_1_description_1": {"attribute_name": "ID", "attribute_value_mlt": [{"subitem_description": "2010519", "subitem_description_type": "Other"}]}, "item_1_description_12": {"attribute_name": "要旨", "attribute_value_mlt": [{"subitem_description": "Gene expression analysis is commonly used to analyze millions of gene ex-\r\npression data points.　Challenging in this process has been the development of\r\nappropriate statistical methods for high-dimensional data.　We propose Sparse\r\nLearner Boosting for gene expression data analysis.　Boosting is performed to\r\nminimize the loss function, although this process can cause overfitting when\r\na large number of variables are present.　Ordinary boosting utilizes all of the\r\npotential weak learners in a given data set and constructs a decision rule.　The\r\nfundamental idea of Sparse Learner Boosting is to reduce the complexity of\r\nthe decision rule by using fewer weak learners than is usually required.　This\r\nreduction prevents overfitting and improves performance during classification.\r\nNumerical studies support this modification for high-dimensional data, such\r\nas that obtained from gene expression analysis. We show that the proposed\r\nmodification improves the performance of ordinary boosting methods. We\r\nalso review another problem in high-dimensional data.　Sparser solutions are\r\ndesirable from the view point of simple classification modeling and ease of\r\ninterpretation however there is no unique sparse solution in any single classifi-\r\ncation problem.　The possible combination of gene sets out of millions of gene\r\nexpression data is huge.　We show the existence of multiple optimum gene sets\r\nand consider the possible solutions.", "subitem_description_type": "Other"}]}, "item_1_description_18": {"attribute_name": "フォーマット", "attribute_value_mlt": [{"subitem_description": "application/pdf", "subitem_description_type": "Other"}]}, "item_1_description_7": {"attribute_name": "学位記番号", "attribute_value_mlt": [{"subitem_description": "総研大甲第1383号", "subitem_description_type": "Other"}]}, "item_1_select_14": {"attribute_name": "所蔵", "attribute_value_mlt": [{"subitem_select_item": "有"}]}, "item_1_select_16": {"attribute_name": "複写", "attribute_value_mlt": [{"subitem_select_item": "印刷物から複写可"}]}, "item_1_select_17": {"attribute_name": "公開状況", "attribute_value_mlt": [{"subitem_select_item": "全文公開可"}]}, "item_1_select_8": {"attribute_name": "研究科", "attribute_value_mlt": [{"subitem_select_item": "複合科学研究科"}]}, "item_1_select_9": {"attribute_name": "専攻", "attribute_value_mlt": [{"subitem_select_item": "15 統計科学専攻"}]}, "item_1_text_10": {"attribute_name": "学位授与年度", "attribute_value_mlt": [{"subitem_text_value": "2010"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "PRITCHARD, Mari", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "0", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2016-02-17"}], "displaytype": "simple", "download_preview_message": "", "file_order": 0, "filename": "甲1383_要旨.pdf", "filesize": [{"value": "169.2 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_11", "mimetype": "application/pdf", "size": 169200.0, "url": {"label": "要旨・審査要旨", "url": "https://ir.soken.ac.jp/record/2169/files/甲1383_要旨.pdf"}, "version_id": "936c5b74-71fe-468d-b353-4c8733813561"}, {"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2016-02-17"}], "displaytype": "simple", "download_preview_message": "", "file_order": 1, "filename": "甲1383_本文.pdf", "filesize": [{"value": "996.0 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_11", "mimetype": "application/pdf", "size": 996000.0, "url": {"label": "本文", "url": "https://ir.soken.ac.jp/record/2169/files/甲1383_本文.pdf"}, "version_id": "55045d04-4e2a-48d9-b5cd-3b30891727f4"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "thesis", "resourceuri": "http://purl.org/coar/resource_type/c_46ec"}]}, "item_title": "Boosting method via the sparse learner approach for high-dimensional gene expression data", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Boosting method via the sparse learner approach for high-dimensional gene expression data"}, {"subitem_title": "Boosting method via the sparse learner approach for high-dimensional gene expression data", "subitem_title_language": "en"}]}, "item_type_id": "1", "owner": "21", "path": ["17"], "permalink_uri": "https://ir.soken.ac.jp/records/2169", "pubdate": {"attribute_name": "公開日", "attribute_value": "2011-06-03"}, "publish_date": "2011-06-03", "publish_status": "0", "recid": "2169", "relation": {}, "relation_version_is_last": true, "title": ["Boosting method via the sparse learner approach for high-dimensional gene expression data"], "weko_shared_id": -1}