複數模糊類神經系統於多類別分類問題之研究

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羅偉成(Wei-Cheng Lo) 查詢紙本館藏

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論文名稱

複數模糊類神經系統於多類別分類問題之研究
(A Study on Multi-class Classification Using Complex Neuro-Fuzzy System)

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摘要(中)

本研究提出一種分類器架構：CNFS-OAA，其為一基於複數模糊類神經系統
(Complex neuro-fuzzy system, CNFS) 的建模程序，透過一對全部(One-against-all, OAA)
方法將資料集分解為多個二類別資料，並以動態探勘模糊法則的方式來處理分類問題。
在CNFS的建模過程中，將使用標準粒子群演算法(Standard particle swarm optimization,
SPSO) 來調整其前鑑部參數，與遞迴最小平方估計法(Recursive least squares estimator,
RLSE) 來調整其後鑑部參數。而CNFS的法則探勘方式，其法則數量將依據訓練階段之
分類正確率來動態增加。當訓練正確率未達到門檻值時，將會探勘更多的法則，並將已
經可被正確分類的資料從訓練資料集中移除。為了提升建模效率，本研究將使用F-score
屬性選取方式，來降低資料集的維度，在維持甚至提升正確率的情形下節省計算成本。
最後，從UCI機器學習資料庫取得十一個真實世界的資料集，來檢驗本研究提出的方法，
並與其他學者提出的分類演算法比較。從實驗結果可以發現，本研究所提出的方法在分
類正確率上能擁有良好的表現。

摘要(英)

In this study, a classifier called CNFS-OAA has been presented, where modeling
procedure is based on complex neuro-fuzzy system (CNFS). The training dataset are divided
into multiple binary-class subsets gradually by using one-against-all (OAA), as the training
procedure proceeds. The fuzzy rules of CNFS are mined dynamically. In the CNFS modeling
procedure, the method of standard particle swarm optimization (SPSO) is used to adjust the
premise parameters and the algorithm of recursive least squares estimator (RLSE) is used to
adapt the consequent parameters. The method of rules mining for CNFS is that the number of
fuzzy IF-THEN rules is incremented dynamically according to the accuracy of classification
while training. More rules will be mined when the training accuracy cannot reach the
threshold, and the tuples classified correctly will be removed from the training dataset. For the
purpose of increasing modeling performance, a method of feature selection called F-score is
used to choose useful features and so to reduce the feature dimensions of dataset. By this way,
the computational cost can be saved while keeping or even improving the accuracy. In this
study, eleven datasets from the UCI machine learning repository have been used to evaluate
the approach proposed. The results by the proposed approach are compared with those by
other noted approaches. The experimental results show that the approach proposed has fine
performance on classification.

關鍵字(中)

★ 多類別分類問題
★ 複數模糊類神經系統
★ 一對全部
★ 混合式學習法
★ F-score
★ 屬性選取
★ 標準粒子群演算法
★ 遞迴最小平方估計法

關鍵字(英)

★ multi-class classification
★ complex neuro-fuzzy system (CNFS)
★ one-against-all
★ hybrid learning
★ F-score
★ feature selection
★ standard particle swarm optimization
★ recursive least squares estimator

論文目次

論文摘要 .................................... i
Abstract.....................................ii
誌謝…......................................iii
目錄…...................................... iv
圖目錄...................................... vi
表目錄......................................vii
符號說明.................................... ix
第1 章緒論 .................................. 1
1.1 研究背景、動機與目的 .................... 1
1.2 研究方法 ................................ 3
1.3 論文架構 ................................ 3
第2 章文獻探討 .............................. 4
2.1 複數模糊集合 ............................ 4
2.2 複數類神經模糊系統 ...................... 7
2.3 F-score............................. .....9
2.4 參數學習 (一) ：標準粒子群最佳化演算法.. 10
2.5 參數學習 (二) ：遞迴最小平方估計法...... 14
2.6 資料前處理：一對全部 ................... 16
2.7 動態法則探勘 ........................... 17
第3 章系統架構 ............................. 18
3.1 CNFS-OAA 模型........................... 18
3.2 資料前處理 ............................. 19
3.3 法則探勘 ............................... 20
3.4 資料修正 ............................... 23
3.5 分類器測試 ............................. 23
第4 章實驗 ................................. 24
4.1 實驗一：威斯康辛州乳癌原始資料集 ....... 26
4.2 實驗二：國會投票紀錄資料集.............. 29
4.3 實驗三：繁殖力資料集 ................... 32
4.4 實驗四：心臟超音波檢查資料集 ........... 35
4.5 實驗五：帕金森氏症資料集................ 38
4.6 實驗六：鳶尾花資料集 ................... 42
4.7 實驗七：肺癌資料集 ..................... 45
4.8 實驗八：葡萄酒資料集 ................... 47
4.9 實驗九：Hayes-Roth 資料集............... 50
4.10 實驗十：乳房組織資料集 ................ 53
4.11 實驗十一：動物園資料集................. 56
4.12 實驗結果小結 .......................... 60
第5 章討論與結論 ........................... 61
5.1 討論 ................................... 61
5.1.1 其他建模方式 ......................... 63
5.1.2 研究限制 ............................. 64
5.2 結論 ................................... 65
第6 章未來研究方向 ......................... 66
參考文獻.................................... 67

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指導教授

李俊賢(Chunshien Li)

審核日期

2013-7-12

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