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The 3D PCP \u003cbr /\u003e proposed in this dissertation can display several characteristics of multiple variables \u003cbr /\u003e simultaneously. First, it can show all the information of each observation at a glance. \u003cbr /\u003e Second, it can make some nonlinear structures in data clear. Finally, it is useful to \u003cbr /\u003e find piecewise linear relationships of variables and their conditions. \u003cbr /\u003e The basic idea of the 3D PCP has already appeared in the parallel coordinate plot. \u003cbr /\u003e The parallel coordinate plot can visualize multi dimensional data on a two dimensional \u003cbr /\u003e plane. Coordinates of all variables are set in parallel. In the standard form of parallel \u003cbr /\u003e coordinate plot the bottom position of each axis corresponds to the minimum value of \u003cbr /\u003e each variable, and the top to the maximum value. One observation corresponds to one \u003cbr /\u003e set of connected lines. Parallel coordinate plot can shows the characteristic between \u003cbr /\u003etwo adjoining axes of variables directly. If two variables have a correlation coefficient \u003cbr /\u003eof 1, lines expressing observations are located horizontally. If two variables have \u003cbr /\u003ecorrelation coefficient l, lines of them cross in one point in the middle between two \u003cbr /\u003eaxes. \u003cbr /\u003e However, relations between two variables whose positions are apart more than two \u003cbr /\u003eaxes are not clearly shown immediately. Another serious problem of static parallel \u003cbr /\u003ecoordinate plot is that it is not easy to distinguish one observation from another when \u003cbr /\u003ethe number of observations is large. To solve these problems, several interactive \u003cbr /\u003etechniques have been developed including highlighting by brushing operations. The \u003cbr /\u003e3D PCP can show the same effect as the highlighting by brushing operation in a \u003cbr /\u003eparallel coordinate plot by extending it into 3dimensional space. We choose one \u003cbr /\u003evariable as a reference variable, usually a response variable. The 3D PCP places \u003cbr /\u003econnected lines expressing observations in 3dimensional spaces by sorting them \u003cbr /\u003eaccording to the values of the reference variable. This observationwise 3D PCP \u003cbr /\u003erepresentation is useful for illustrating the characteristics of observations such as \u003cbr /\u003eoutliers. \u003cbr /\u003e The 3D PCP has another representation in which values of observations on each \u003cbr /\u003evariable are connected by lines. It is called variablewise connection representation \u003cbr /\u003eand is useful to see relations between the reference variable and other variables. For \u003cbr /\u003eexample, the connected lines expressing the variable which has strong linear \u003cbr /\u003erelationships with the reference variable are located around a straight line expressing \u003cbr /\u003ethe reference variable. It is well known that the scatterplot matrix can show \u003cbr /\u003erelationships between variables clearly. However, if the number of variables is large, \u003cbr /\u003ethe single scatterplot elements become too small to be seen properly. The 3D PCP can \u003cbr /\u003eshow more variables than a scatterplot matrix. It is sometimes more suitable to show \u003cbr /\u003echaracteristics of data simultaneously than using a static representation of scatterplot \u003cbr /\u003ematrlx. \u003cbr /\u003e We note that the 3D PCP can detect particular nonlinear relations, i.e. interaction \u003cbr /\u003eby two variables, through observationwise connection representation. This \u003cbr /\u003erelationship is detected by the special pattern of the angles of connected lines \u003cbr /\u003eexpressing observations. As explained earlier, a correlation coefficient near 1 or 1 \u003cbr /\u003ebetween two variables whose axes are adjacent produces parallel or crossing patterns \u003cbr /\u003ein parallel coordinate plots. Similar patterns can be detected in 3D PCP. If we find the \u003cbr /\u003echange of such patterns at specific values of the reference variable, we conclude that \u003cbr /\u003ethe structures of data are different for each region of the reference variables. In such \u003cbr /\u003ecases, it is natural to divide the data into several groups in which the structures have \u003cbr /\u003eno more changes. We propose to draw many 3D PCPs corresponding to the groups \u003cbr /\u003esimultaneously and use a lattice layout, which places many graphs on a grid. We show \u003cbr /\u003ethat they are useful to identify the interaction between two variables by analyzing \u003cbr /\u003esimulation and real data. \u003cbr /\u003e We realize our 3D PCP by using the Java language. Java has several advantages for \u003cbr /\u003eimplementing modern data visualization methods. It is a pure object oriented \u003cbr /\u003eprogramming language and has welldesigned standard graphics libraries which are \u003cbr /\u003euseful to realize 2D and 3D graphics and interactive graphical user interfaces. These \u003cbr /\u003elibraries can work as useful components of statistical graphics and be in incorporated \u003cbr /\u003eby using socalled design patterns. Design patterns are suggested solutions to common \u003cbr /\u003eproblems often appearing in objectoriented software development. Our \u003cbr /\u003eimplementation is based on several design patterns for generality and reusability. Our \u003cbr /\u003esoftware enables us to analyze data by utilizing advanced interactive operations given \u003cbr /\u003eby Java. We show that the 3D PCP and the software are expected to lead to new \u003cbr /\u003eachievements in the field of data visualization. \u003cbr /\u003e This dissertation is set out as following. Chapter 1 surveys issues of information \u003cbr /\u003evisualization and basic statistical graphics used in multivariate data visualization \u003cbr /\u003esuch as scatterplot, scatterplot matrix, 3D scatterplot and parallel coordinate plot. It \u003cbr /\u003ealso discusses dynamic techniques of data visualization and existing software products \u003cbr /\u003efor data visualization such as Mondrian, ParallAX, and GGobi. In Chapter 2, 2D \u003cbr /\u003eparallel coordinate plots are discussed. Important issues at visual data analysis with \u003cbr /\u003eparallel coordinate pIot are considered. We introduce several existing works for \u003cbr /\u003eextending parallel coordinate plot into 3\u0027dimensional spaces. In Chapter 3, we discuss \u003cbr /\u003eour extension of parallel coordinate plot into 3dimensional space, and several of its \u003cbr /\u003echaracteristics. We show usefulness of lattice layout to display several 3D PCP at a \u003cbr /\u003etime in Chapter 4. Chapter 5 analyzes three data sets by using our 3D PCP. In \u003cbr /\u003eChapter 6, we explain details of our software design. Finally, concluding remarks are \u003cbr /\u003egiven in Chapter 7. \u003cbr /\u003e", "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1149号", "subitem_description_type": "Other"}]}, "item_1_select_14": {"attribute_name": "所蔵", "attribute_value_mlt": [{"subitem_select_item": "有"}]}, "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": "2007"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "SHIRAISHI, Yuichi", "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": "20160217"}], "displaytype": "simple", "download_preview_message": "", "file_order": 0, "filename": "甲1149_要旨.pdf", "filesize": [{"value": "360.6 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_11", "mimetype": "application/pdf", "size": 360600.0, "url": {"label": "要旨・審査要旨", "url": "https://ir.soken.ac.jp/record/788/files/甲1149_要旨.pdf"}, "version_id": "13d76150bae94c8b975eddb02538ca2d"}]}, "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": "Gametheoretical and statistical study on combination of binary classifiers for multiclass classification", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Gametheoretical and statistical study on combination of binary classifiers for multiclass classification"}, {"subitem_title": "Gametheoretical and statistical study on combination of binary classifiers for multiclass classification", "subitem_title_language": "en"}]}, "item_type_id": "1", "owner": "1", "path": ["17"], "permalink_uri": "https://ir.soken.ac.jp/records/788", "pubdate": {"attribute_name": "公開日", "attribute_value": "20100222"}, "publish_date": "20100222", "publish_status": "0", "recid": "788", "relation": {}, "relation_version_is_last": true, "title": ["Gametheoretical and statistical study on combination of binary classifiers for multiclass classification"], "weko_shared_id": 1}
Gametheoretical and statistical study on combination of binary classifiers for multiclass classification
https://ir.soken.ac.jp/records/788
https://ir.soken.ac.jp/records/788be43f5f1be3c498e899bcd9d10d744f3
名前 / ファイル  ライセンス  アクション 

要旨・審査要旨 (360.6 kB)

Item type  学位論文 / Thesis or Dissertation(1)  

公開日  20100222  
タイトル  
タイトル  Gametheoretical and statistical study on combination of binary classifiers for multiclass classification  
タイトル  
言語  en  
タイトル  Gametheoretical and statistical study on combination of binary classifiers for multiclass classification  
言語  
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 
白石, 友一
× 白石, 友一 

フリガナ 
シライシ, ユウイチ
× シライシ, ユウイチ 

著者 
SHIRAISHI, Yuichi
× SHIRAISHI, Yuichi 

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  
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1149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  
学位授与年月日  20080319  
学位授与年度  
2007  
要旨  
内容記述タイプ  Other  
内容記述  The three dimensional parallel coordinate plot (3D PCP) is a visualization method <br /> to detect hidden information in data by using human spatial perception. The 3D PCP <br /> proposed in this dissertation can display several characteristics of multiple variables <br /> simultaneously. First, it can show all the information of each observation at a glance. <br /> Second, it can make some nonlinear structures in data clear. Finally, it is useful to <br /> find piecewise linear relationships of variables and their conditions. <br /> The basic idea of the 3D PCP has already appeared in the parallel coordinate plot. <br /> The parallel coordinate plot can visualize multi dimensional data on a two dimensional <br /> plane. Coordinates of all variables are set in parallel. In the standard form of parallel <br /> coordinate plot the bottom position of each axis corresponds to the minimum value of <br /> each variable, and the top to the maximum value. One observation corresponds to one <br /> set of connected lines. Parallel coordinate plot can shows the characteristic between <br />two adjoining axes of variables directly. If two variables have a correlation coefficient <br />of 1, lines expressing observations are located horizontally. If two variables have <br />correlation coefficient l, lines of them cross in one point in the middle between two <br />axes. <br /> However, relations between two variables whose positions are apart more than two <br />axes are not clearly shown immediately. Another serious problem of static parallel <br />coordinate plot is that it is not easy to distinguish one observation from another when <br />the number of observations is large. To solve these problems, several interactive <br />techniques have been developed including highlighting by brushing operations. The <br />3D PCP can show the same effect as the highlighting by brushing operation in a <br />parallel coordinate plot by extending it into 3dimensional space. We choose one <br />variable as a reference variable, usually a response variable. The 3D PCP places <br />connected lines expressing observations in 3dimensional spaces by sorting them <br />according to the values of the reference variable. This observationwise 3D PCP <br />representation is useful for illustrating the characteristics of observations such as <br />outliers. <br /> The 3D PCP has another representation in which values of observations on each <br />variable are connected by lines. It is called variablewise connection representation <br />and is useful to see relations between the reference variable and other variables. For <br />example, the connected lines expressing the variable which has strong linear <br />relationships with the reference variable are located around a straight line expressing <br />the reference variable. It is well known that the scatterplot matrix can show <br />relationships between variables clearly. However, if the number of variables is large, <br />the single scatterplot elements become too small to be seen properly. The 3D PCP can <br />show more variables than a scatterplot matrix. It is sometimes more suitable to show <br />characteristics of data simultaneously than using a static representation of scatterplot <br />matrlx. <br /> We note that the 3D PCP can detect particular nonlinear relations, i.e. interaction <br />by two variables, through observationwise connection representation. This <br />relationship is detected by the special pattern of the angles of connected lines <br />expressing observations. As explained earlier, a correlation coefficient near 1 or 1 <br />between two variables whose axes are adjacent produces parallel or crossing patterns <br />in parallel coordinate plots. Similar patterns can be detected in 3D PCP. If we find the <br />change of such patterns at specific values of the reference variable, we conclude that <br />the structures of data are different for each region of the reference variables. In such <br />cases, it is natural to divide the data into several groups in which the structures have <br />no more changes. We propose to draw many 3D PCPs corresponding to the groups <br />simultaneously and use a lattice layout, which places many graphs on a grid. We show <br />that they are useful to identify the interaction between two variables by analyzing <br />simulation and real data. <br /> We realize our 3D PCP by using the Java language. Java has several advantages for <br />implementing modern data visualization methods. It is a pure object oriented <br />programming language and has welldesigned standard graphics libraries which are <br />useful to realize 2D and 3D graphics and interactive graphical user interfaces. These <br />libraries can work as useful components of statistical graphics and be in incorporated <br />by using socalled design patterns. Design patterns are suggested solutions to common <br />problems often appearing in objectoriented software development. Our <br />implementation is based on several design patterns for generality and reusability. Our <br />software enables us to analyze data by utilizing advanced interactive operations given <br />by Java. We show that the 3D PCP and the software are expected to lead to new <br />achievements in the field of data visualization. <br /> This dissertation is set out as following. Chapter 1 surveys issues of information <br />visualization and basic statistical graphics used in multivariate data visualization <br />such as scatterplot, scatterplot matrix, 3D scatterplot and parallel coordinate plot. It <br />also discusses dynamic techniques of data visualization and existing software products <br />for data visualization such as Mondrian, ParallAX, and GGobi. In Chapter 2, 2D <br />parallel coordinate plots are discussed. Important issues at visual data analysis with <br />parallel coordinate pIot are considered. We introduce several existing works for <br />extending parallel coordinate plot into 3'dimensional spaces. In Chapter 3, we discuss <br />our extension of parallel coordinate plot into 3dimensional space, and several of its <br />characteristics. We show usefulness of lattice layout to display several 3D PCP at a <br />time in Chapter 4. Chapter 5 analyzes three data sets by using our 3D PCP. In <br />Chapter 6, we explain details of our software design. Finally, concluding remarks are <br />given in Chapter 7. <br />  
所蔵  
値  有 