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研究生:吳沐穎
研究生(外文):Mu-YingWu
論文名稱:簡易貝氏分類器中廣義狄氏先驗分配應用於基因序列資料分類之研究
論文名稱(外文):Generalized Dirichlet Priors for Naïve Bayesian Classifiers with Multinomial Models in Classifying Gene Sequence Data
指導教授:翁慈宗翁慈宗引用關係
指導教授(外文):Tzu-Tsung Wong
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:54
中文關鍵詞:狄氏分配基因序列資料分類廣義狄氏分配簡易貝氏分類器
外文關鍵詞:Dirichlet distributiongene sequence data classificationgeneralized Dirichlet distributionnaïve Bayesian classifier
相關次數:
  • 被引用被引用:3
  • 點閱點閱:191
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
隨時光更佚,生物學家已不在受限於從實驗室的培養皿中觀察物種,隨著多源基因體學以及相關技術的發展之下,從日常生活中獲得物種樣本已非難事,但伴隨此技術而來的問題,卻困擾著相關研究學者;雖然透過後者的方法可以更接近真實的去了解物種之間的關聯性,以及其所依存的聚落關係,但透過該技術取得的基因序列樣本卻不再如前者方法一般單純。由於透過新的定序技術,科學家可從自然環境中取得大量的序列樣本,但其通常混雜許多物種的基因序列片段,要如何找出其原來所屬的物種類別,儼然成了一項新的挑戰。而本研究即希望透過簡易貝氏分類器的快速運算特性,來幫助從事此一研究的學者解決對於基因序列作分類的問題,幫助其找出各個序列片段的來源歸屬。但由於基因序列資料在不同的綱目之下,各有不同的物種分類,以及在比較下方的階層中,所需分辨的物種數量尤其龐大,且各個序列中所能取得的各個特徵又不甚明顯,在此情況之下,希望藉由更適合的先驗分配參數設定方式來增進簡易貝氏分類器的分類正確率。而本研究所採用的先驗分配-狄氏分配、廣義狄氏分配,其已被證明較適用於簡易貝氏分類器的先驗分配估計,並希望藉由該分配的原理,實作一適合用於前述類別數量眾多,而樣本特徵組合中又有眾多小出現機率值或幾乎沒有出現的分類問題。並在實證研究中使用兩個資料檔進行驗證,並於結果顯示上述先驗分配確實對分類正確率能有所提升,尤其是在未調整前分類正確率較低的資料檔得到較大的改善。

With the passing of time, biologists are no longer limited to make observations on Petri dishes in labs. Nowadays, they can easily obtain samples from the natural world by using the new technology developed for metagenomics. Although the new technology is helpful in studying the relationships among species and the places where they live, samples obtained in this way cannot be analyzed by traditional methods. This research attempts to propose a new operational mechanism for naïve Bayesian classifiers to classify gene sequence data for biologists. Since the number of class values or species is generally over one hundred, and the number of features extracted from gene sequence data can be more than ten thousand, the information carried by a feature for classification will be relatively little. In this case, priors can play an important role in the operation of the naïve Bayesian classifier. This research adopts Dirichlet and generalized Dirichlet distributions that have been shown to be appropriate priors for improving the performance of the naïve Bayesian classifier to enhance its prediction accuracy on gene sequence data. The experimental results on two gene sequence data sets demonstrate that priors do helpful in classifying gene sequence instances, and that a significant improvement can be achieved in a gene sequence data set in which the original prediction accuracy is poor.
摘 要 I
Abstract II
致 謝 III
目 錄 IV
表目錄 VI
圖目錄 VII
符號表 VIII
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 研究流程 3
第二章 文獻探討 4
2.1 簡易貝氏分類器 4
2.1.1 基本運作原理 4
2.1.2 簡易貝氏分類器的應用 6
2.1.2.1 簡易貝氏分類器應用於文件分類 6
2.1.2.2 簡易貝氏分類器應用於基因序列分類 9
2.2平滑常數 12
2.3 狄氏分配與廣義狄氏分配 14
2.3.1 狄氏分配的計算公式 14
2.3.2 廣義狄氏分配的計算公式 16
2.3.3 狄氏與廣義狄氏分配的關係 16
第三章 研究方法 18
3.1 基因序列分類流程與敘述 18
3.2 基因序列資料的前置處理 21
3.3多項式模型 22
3.4 先驗分配參數的調整以及修正方法 22
3.5 尋找最佳先驗分配參數的方法 24
3.5.1 狄氏分配參數的尋找方法 24
3.5.2 廣義狄氏分配參數的尋找方法 25
3.6 驗證方式 30
第四章 實證研究 32
4.1 資料檔介紹 32
4.2 狄氏分配之實證結果 32
4.3 廣義狄氏分配之實證結果 34
4.4 小結 39
第五章 結論與建議 40
參考文獻 42
附錄一 狄氏分配正確率變化表-Bacteria資料檔 46
附錄二 狄氏分配正確率變化表-Fungi資料檔 47
附錄三 廣義狄氏分配正確率變化表- Bacteria資料檔 49
附錄四 廣義狄氏分配正確率變化表- Fungi資料檔 51

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