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研究生:賴隆平
研究生(外文):Lai, Long Ping
論文名稱:官員職等陞遷分類預測之研究
論文名稱(外文):Classification prediction on government official’s rank promotion
指導教授:劉吉軒劉吉軒引用關係
指導教授(外文):Liu, Jyi Shane
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊科學學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:98
語文別:中文
論文頁數:141
中文關鍵詞:資料探勘支撐向量機決策樹
外文關鍵詞:Data MiningSVMSupport Vector MachineDTDecision Tree
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公務人員的人事陞遷是一個複雜性極高,其中隱藏著許多不變的定律及過程,長官與部屬、各公務人員人之間的關係,更是如同蜘蛛網狀般的錯綜複雜,而各公務人員的陞遷狀況,更是隱藏著許多派系之間的鬥爭拉扯連動,或是提攜後進的過程,目前透過政府公開的總統府公報-總統令,可以清楚得知所有公務人員的任職相關資料,其中包含各職務之間的陞遷、任命、派免等相關資訊,而每筆資料亦包含機關、單位、職稱及職等資料,可以提供各種研究使用。

本篇係整理出一種陞遷序列的資料模型來進行研究,透過資料探勘的相關演算法-支撐向量機(Support Vector Machine,簡稱SVM)及決策樹(Decision Tree)的方式,並透過人事的領域知識加以找出較具影響力的屬性,來設計實驗的模型,並使用多組模型及多重資料進行實驗,透過整體平均預測結果及圖表方式來呈現各類別的預測狀況,再以不同的屬性資料來運算產生其相對結果,來分析其合理性,最後再依相關數據來評估此一方法的合理及可行性。

透過資料探勘設計的分類預測模型,其支撐向量機與決策樹都具有訓練量越大,展現之預測結果也愈佳之現象,這跟一般模型是相同的,而挖掘的主管職務屬性參數及關鍵屬性構想都跟人事陞遷的邏輯不謀而合,而預測結果雖各有所長,但整體來看則為支撐向量機略勝一籌,惟支撐向量機有一狀況,必須先行排除較不具影響力之屬性參數資料,否則其產生超平面的邏輯運算過程將產生拉扯作用,導致影響其預測結果;而決策樹則無是類狀況,且其應用較為廣泛,可以透過宣告各屬性值的類型,來進行不同屬性資料類型的分類實驗。

而透過支撐向量機與決策樹的產生的預測結果,其正確率為百分之77至82左右,如此顯示出國內中高階文官的陞遷制度是有脈絡可循的,其具有一定的制度規範及穩定性,而非隨意的任免陞遷;如此透過以上資料探勘的應用,藉著此特徵研究提供公務部門在進行人力資源管理、組織發展、陞遷發展以及組織部門精簡規劃上,作為調整設計參考的一些相關資訊;另透過一些相關屬性的輸入,可提供尚在服務的公務人員協助其預估陞遷發展的狀況,以提供其進行相關生涯規劃。
The employee promotion is a highly complexity task in Government office, it include many invariable laws and the process, between the senior officer and the subordinate, various relationships with other government employees, It’s the similar complex with the spider lattice, and it hides many clique's struggles in Government official’s promotion, and help to process the promote for the junior generation, through the government public presidential palace - presidential order, it‘s able to get clearly information about all government employees’ correlation data, include various related information like promotion, recruitment , and each data also contains the instruction, like the job unit, job title and job rank for all research reference.

It organizes a promoted material model to conduct the research, by the material exploration's related calculating method – Support Vector Machine (SVM) and the decision tree, and through by knowledge of human resource to discover the influence to design the experiment's model, and uses the multi-group models and materials to process, and by this way , it can get various categories result by overall average forecasting and the graph, then operates by different attribute material to get relative result and analyzes its rationality, finally it depends on the correlation data to re-evaluate its method reasonable and feasibility.

To this classification forecast model design, the SVM and the decision tree got better performance together with the good training quality, it’s the same with the general model, and it’s the same view to find the details job description for senior management and employee promotion, however the forecasting result has their own strong points, but for the totally, the SVM is slightly better, only if any accidents occurred, it needs to elimination the attribute parameter material which is not have the big influence, otherwise it will have the planoid logic operation process to produce resist status, and will affect its forecasting result, but the decision tree does not have this problem, and its application is more widespread, it can through by different type to make the different experiment.

The forecasting result through by SVM and decision tree, its correction percentage can be achieved around 77% - 82% , so it indicated the high position level promotion policy should be have its own rules to follow, it has certain system standard and the stability, but non-optional promoted, so trough by the above data mining, follow by this characteristic to provide Government office to do the Human resource management, organization development, employee promotion and simplify planning to the organization, takes the re-design information for reference, In addition through by some related attribute input, it may provide the government employee who is still on duty and assist them to evaluate promotion development for future career plan.
第 一 章 緒 論 1
1.1 簡介 1
1.2 研究背景與動機 3
1.3 研究方法 3
1.4 本論文的貢獻 5
1.5 研究範圍與限制 6
1.6 論文架構 6
第 二 章 文獻探討 9
2.1 資料探勘的相關知識 10
2.2 支撐向量器(SUPPORT VECTOR MACHINE, SVM)的演算法 14
2.3 決策樹(DECISION TREE)的演算法 19
2.4 模型的訓練方式 27
2.5 評估方法 29
2.6 小結 32
第 三 章 職務陞遷領域序列分析 33
3.1 研究架構 33
3.2 資料庫模型的建立 36
3.2.1 資料庫欄位內容分析 36
3.2.2 資料內容需求整合 37
3.2.3 可供研究之屬性資料分析建立 38
3.2.4 文職陞遷序列職等資料建立 42
3.2.5 不合理之陞遷序列資料刪除 44
3.2.6 資料分群(類) 49
3.2.7 主管職務屬性 56
3.2.8 完整陞遷序列資料模型 61
3.3 分類訓練分析 66
3.3.1 訓練資料內容 66
3.3.2 分類模型之訓練模式 68
3.3.3 預測結果之分析方式 70
3.4 小結 72
第 四 章 職務陞遷序列之二元分類 73
4.1 資料模型屬性說明 73
4.2 資料挑選暨驗證方式說明 76
4.3 支撐向量機 (SVM) 分類 76
4.3.1 支撐向量機 (SVM) 分類過程說明 77
4.3.2 分類結果說明 79
4.4 決策樹(DECISION TREE)之(C4.5)分類 79
4.4.1 決策樹 (DECISION TREE) 分類過程說明 80
4.4.2 分類結果說明 81
4.5 小結 86
第 五 章 分類預測評估 87
5.1 資料庫來源及實驗模型說明 87
5.2 實驗設計規劃 88
5.2.1 分類模型分析 90
5.2.2 實驗模型的屬性選擇 95
5.2.3 資料庫產生分類資料樣式 97
5.2.4 訓練方式模組設計 98
5.2.5 實驗作業及評估步驟說明 101
5.3 使用支撐向量機產生預測結果 101
5.3.1 支撐向量機之平均訓練錯誤率報表 102
5.3.2 支撐向量機之平均預測錯誤率報表 104
5.3.3 支撐向量機預測評估暨分析說明 106
5.4 使用決策樹產生預測結果 112
5.4.1 決策樹之平均訓練錯誤率報表 112
5.4.2 決策樹之平均預測錯誤率報表 115
5.4.3 決策樹預測評估暨分析說明 117
5.5 正反面資料預測結果分析 123
5.6 實驗分析暨小結 124
第 六 章 結論和未來方向 133
6.1 研究結論 133
6.2 未來研究建議 135
6.3 未來研究方向 136
第 七 章 參考文獻 139
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