臺灣博碩士論文加值系統

隨著多源基因體學與定序技術的發展，更加重視分類器在處理高維度基因序列資料的效能，而簡易貝氏分類器具有高效率和容易使用的特性，已經被應用於處理基因序列資料的領域上。且先驗分配參數的使用已被證明可以用來提升多項式模型結合簡易貝氏分類器處理基因序列資料在分類正確率上的表現。基因序列資料的類別值數量龐大，有些類別值所屬資料樣本較少，限制了分類正確率往上提升的可能性，因此本研究採用狄氏先驗分配，並以參數估算的方式取代傳統耗時的搜尋參數過程，而先驗分配參數的估算以特徵為單位進行，從特徵中計算出共變異數矩陣，再從共變異數矩陣挑選各類別值較合適的統計量，利用3 種選取參數組合的方法與2 種搜尋參數組合的方法以求得更合適的參數組合。實驗結果顯示本研究之方法可以改善類別值所屬資料樣本過少的問題，且其中2 種參數搜尋方法能找到更合適的參數組合，在分類正確率上能獲得改善，但需犧牲一定程度的運算時間。
為持續提升分類正確率，本研究將狄氏分配之預測能力較差且樣本數量較多之類別值改採用廣義狄氏分配之參數進行分類預測，實驗結果顯示對於持續提升分類正確率的效果有限，其主要原因來自類別值數量龐大且各類別值所屬樣本偏少所造成。

Due to the development of metagenomics and sequencing, analysts pay more attention to the effectiveness of classification algorithms in processing high dimensional gene sequence data. Naïve Bayesian classifiers are a popular tool for classifying high dimensional gene sequence data because of its computational efficiency and easy implementation. Setting proper parameters for priors have been shown to be an effective way for improving the performance of the naïve Bayesian classifier with multinomial models, called multinomial naïve Bayesian classifiers, in gene sequence classification. Since the number of class values in a gene sequence data set is huge, and the number of instances for many class values is less than ten, the possibility of improving the classification accuracy of gene sequence data is generally limited. In this study, the covariance matrices for features are first calculated from available gene sequence data. Then several ways are proposed to set and search the parameters of Dirichlet priors for the naïve Bayesian classifiers with multinomial model. The experimental results on two gene sequence sets demonstrate that our proposed methods can improve the prediction accuracy of the multinomial naïve Bayesian classifier in acceptable computational time. The generalized Dirichlet priors are then introduced for the class values with low accuracy and large number of instances. The experimental results on the same gene sequence sets show that the improvement on prediction accuracy is limited because the number of class values is huge and the number of instances in many class values is small.

摘要 I
誌謝 VI
目錄 VII
表目錄 IX
圖目錄 X
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究架構 2
第二章 文獻探討 3
2.1 簡易貝氏分類器 3
2.1.1 簡易貝氏分類器運作原理 3
2.1.2 簡易貝氏分類器在文件分類的機率模型 4
2.2 多源基因體學 5
2.2.1 基因序列資料的樣貌及特徵萃取 6
2.2.2 多源基因體學的資料探勘方法 7
2.3 先驗分配 9
2.3.1 狄氏分配 9
2.3.2 廣義狄氏分配 11
2.4 小結 12
第三章 研究方法 14
3.1 資料前置處理 15
3.2 共變異數矩陣計算19
3.2.1 特徵-共變異數矩陣計算 19
3.2.2 特徵組-共變異數矩陣計算 20
3.3 先驗分配參數的估算方法 22
3.3.1 狄氏分配之參數估算方法 22
3.3.2 廣義狄氏分配之參數估算方法 26
3.4 簡易貝氏分類器處理基因序列資料 34
3.4.1 狄氏分配參數結合簡易貝氏分類器運作 35
3.4.2 廣義狄氏分配參數結合簡易貝氏分類器運作 35
3.5 驗證方式 36
第四章 實證研究 37
4.1 資料檔介紹 37
4.2 簡易貝氏分類器結合狄氏分配參數估算方法之實證研究 38
4.2.1 參數選取方法對於參數個數之比較 38
4.2.2 各方法對於正確率與小樣本類別值預測正確筆數之比較 39
4.2.3 各方法運算時間之比較 44
4.3 小結 46
第五章 結論與建議 48
5.1 結論 48
5.2 建議與未來發展 49
參考文獻 50
附錄一 狄氏分配正確率變化表-BACTERIA 資料檔 53
附錄二 狄氏分配正確率變化表-FUNGI 資料檔 54

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劉超瑞，(2013)。應用多項式簡易貝氏分類器於文件分類的推導廣義狄氏分配參數之方法。國立成功大學資訊管理研究所碩士論文。
韓昀達，(2013)。多項式馬可夫簡易貝氏分類器結合狄氏先驗分配於基因序列分類之研究。國立成功大學資訊管理研究所碩士論文。
陳朝友，(2014)。結合多項式馬可夫貝氏分類器與廣義狄氏分配參數估算方法於基因序列分類之研究。國立成功大學資訊管理研究所碩士論文。
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