基於單一與混合特徵選取方法之比較

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姓名

張櫻馨(Ying-Hsin, Chang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

基於單一與混合特徵選取方法之比較

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摘要(中)

在我們現今生活中，我們面臨巨量資料（Big Data）的問題，還需要考慮到資料的即時性，如何在有限的資源與時間之下，進行資料探勘，找出有趣的樣式，我們首要考慮的是資料前處理（Data Pre-processing），將特徵選取處理後的資料應用在分類器，提高模型預測正確率，進而幫助使用者做決策。

本研究為探討特徵選取（Feature Selection）作為資料前處理的步驟，將不相關、冗餘的特徵（資料的屬性）刪除，換句話說，就是將原始資料集利用特徵選取的演算法，萃取出有用的特徵，或是足以代表整個資料集的資料值，並將這些特徵值重新組成一個新的資料集，再丟入SVM 支援向量機分類器中，希望可以透過特徵選取的方式，改善模型的正確率與執行的效能。

目前大部分的特徵選取大多為單一（競爭式）特徵選取，本研究想加入資訊融合（Information Fusion）的概念，將實驗設計為UCI 公開資料集與其他公開資料集中，取得28 個完整資料集，進行單一（競爭式）特徵選取與混合式資料選取的比較，進一步探討不同維度、類型的資料對於不同方式的特徵選取的影響，以提出資訊融合（Information Fusion）概念的混合式特徵選取是否能幫助處理各種類型的資料集，並可大幅度的提升預測模型的正確率。

摘要(英)

In our current life, we not only face the huge data （Big Data） problem, but also need to take into account the immediacy of information. Under limited resources and
time, it is important to know how to perform data mining to find interesting style. We first consider data pre-processing for feature selection, and apply the selected data to construct the classifier, which could improve the classificaiton accuracy of the model, and help users make decisions.

In this thesis, we discuss the feature selection as the preprocessing step, and remove irrelevant and redundant features （ attributes of the data） from a given dataset. In other words, the feature selection algorithm is used to idenitfy useful or represenative attributes
from the entire data set. We reassemble these attributes into a new data set and then use the support vector machine classifier to improve the correctness and efficiency of the model.

Since most related studies only focus on single （competitive） feature selection, this thesis applies the concept of information fusion for multiple feature selection results. The experiments are based on 28 UCI public datasets. The purpose of this thesis is to
combine multiple feature selection methods. Under different dimensions and data types of information, we are able to understand whether combininng different feature selection results can perform better than single results in terms of classificaiton performance.

關鍵字(中)

★ 資料探勘
★ 機器學習
★ 資訊融合
★ 特徵選取
★ 支援向量機

關鍵字(英)

★ KDD
★ Machine Learning
★ Information Fusion
★ Feature Selection
★ Support Vector Machines

論文目次

摘要 ..... i
Abstract ..... ii
誌謝 ..... iii
目錄 ...... iv
圖目錄 ..... vi
表目錄 ...... vii
第一章緒論 ................................................ 1
1.1 研究背景 ............................................... 1
1.2 研究動機 ............................................... 3
1.3 研究目的 ............................................... 4
1.4 研究架構 ............................................... 5
第二章文獻探討 ............................................ 6
2.1 特徵選取 ............................................... 6
2.1.1 基因演算法（Genetic Algorithm, GA） ................. 10
2.1.2 主成分分析（Principal Components Analysis, PCA） .... 13
2.1.3 決策樹C4.5（Decision Tree C4.5, DT） ................ 14
2.1.4 資訊融合之特徵選取 .................................. 16
2.2 監督式學習於分類器之應用 .............................. 17
2.2.1 監督式學習 .......................................... 17
2.2.2 支援向量機（Support Vector Machines, SVM） .......... 18
第三章實驗方法 ........................................... 20
3.1 實驗架構 .............................................. 20
3.2 實驗參數設定 .......................................... 23
3.2.1 GA 基因演算法（Wrappers） ........................... 23
3.2.2 PCA 主成分分析（Filters） ........................... 24
3.2.3 C4.5 決策樹（Embedded） ............................. 24
3.2.4 SVM 支援向量機 ...................................... 24
3.3 實驗一 ................................................ 24
3.3.1 Baseline ............................................ 25
3.3.2 單一式特徵選取 ...................................... 26
3.3.3 混合式特徵選取 ...................................... 27
3.4 實驗二 ................................................ 28
第四章實驗結果 ........................................... 28
4.1 實驗設定 .............................................. 28
4.1.1 資料集 .............................................. 28
4.1.2 實驗電腦環境 ........................................ 30
4.1.3 模型驗證準則 ........................................ 30
4.2 PCA 主成分分析的資訊保留率之選擇 ...................... 30
4.3 實驗一結果 ............................................ 32
4.3.1 類別型資料單一與混合特徵選取屬性集合大小之比較 ...... 33
4.3.2 數值型資料單一與混合特徵選取屬性集合大小之比較 ...... 35
4.3.3 混合型資料單一與混合特徵選取屬性集合大小之比較 ...... 37
4.3.4 類別型資料（Categorical Data）的SVM 分類器結果 ...... 39
4.3.5 數值型資料（Numeric Data）的SVM 分類器結果 .......... 43
4.3.6 混合型資料（Mixed Data）的SVM 分類器結果 ............ 46
4.3.7 各資料集正確率最佳的方法 ............................ 49
4.3.8 初始資料特徵選取正確率之比較 ........................ 52
4.4 實驗二結果 ............................................ 53
4.4.1 單一與混合特徵選取屬性集合大小之比較 ................ 53
4.4.2 高維度資料的SVM 分類器結果 .......................... 55
4.4.3 各資料集正確率最佳的方法 ............................ 58
4.5 實驗結論 .............................................. 58
第五章結論 ............................................... 60
5.1 結論與貢獻 ............................................ 60
5.2 研究限制與後續研究建議與方向 .......................... 62
參考文獻 .................................................. 64
附錄一特徵選取的結果 ..................................... 69
1.1 研究一單一特徵選取的結果 ............................. 69
1.2 研究二單一特徵選取的結果 ............................. 74

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指導教授

蔡志豐

審核日期

2017-7-4

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