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研究生:呂奇傑
研究生(外文):Chi-Jie Lu
論文名稱:演化式類神經網路分類技術於資料探勘上之應用
論文名稱(外文):Hybrid Neural Network Classification Techniques in the Application of Data Mining
指導教授:邱志洲邱志洲引用關係李天行李天行引用關係
指導教授(外文):Chih-Chou ChiuTian-Shyug Lee
學位類別:碩士
校院名稱:輔仁大學
系所名稱:應用統計學研究所
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:中文
論文頁數:67
中文關鍵詞:資料探勘類神經網路鑑別分析模糊鑑別分析分類問題
外文關鍵詞:Data MiningNeural NetworkLinear Discriminant AnalysisFuzzy Discriminant AnalysiClassification
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由於商業環境不斷快速變遷,迫使企業必須持續求新求變來因應。企業要能夠生存、發展,進而爭取市場競爭的主導權,除了不斷提昇產品的市場價值,更重要的是必須能有效地掌握市場資訊、迅速因應各種來自內外在環境的變化與挑戰。而在資訊科技的推波助瀾下,現今企業所面對的是一個與以往截然不同的競爭環境,不僅企業競爭的強度與速度倍數於以往,激增的市場交易也使得各企業所需儲存與處理的資料量越來越龐大。在這種情況下,企業的焦點已從以往的資料蒐集與整理,轉變成如何有效的利用資料庫來進行資訊的獲取。換言之,企業如何因應外界的競爭,有效的利用資料探勘(data mining)的技術與觀念,在龐大的資料庫中尋找出有價值的隱藏事件,以反應市場或消費者的需求,已成為各企業急於解決的重要議題之一。
本研究嘗試提出兩種演化式類神經網路的分類技術,一為整合傳統的鑑別分析與類神經網路,另一個則是整合模糊鑑別分析與類神經網路來進行資料探勘中判別模式的建立。主要的研究目的乃針對使用類神經網路其學習、收斂速度較慢的缺點進行改善,期望經由傳統鑑別分析與模糊鑑別方法的額外輸入資訊,以提供類神經網路一個良好的空間搜尋起始原點,再透過類神經網路的學習、辨識能力,來發展一個更為快速、精確的判別模式。為驗證提出方法的可行性,本研究針對兩種不同資料進行判別模式的建構,其一為統計教科書中常用的鳶尾花資料;另一個則為台灣某大型銀行的信用卡客戶申請資料。根據研究結果顯示,在二個實證資料下,類神經網路與鑑別分析、模糊鑑別分析之判別績效是優劣互見。而本研究所提之演化式類神經網路分類技術,不管是在鳶尾花或是銀行信用卡的資料上,其判別結果均較單純使用類神經網路者為佳,且網路收斂速度也較快;再者,與鑑別分析及模糊鑑別分析相較,演化式模糊類神經網路分類技術在鳶尾花資料的判別結果上與模糊鑑別分析相同;但在銀行信用卡資料上,判別結果則較模糊鑑別分析為佳。
Data mining is the art of finding patterns in data and is a new approach based on a general recognition that there is undraped value in large databases and utilities data-driven extraction of information. However, it is still not easy to identify the complicate relationship in the huge data set. Moreover, in most case, the estimation of parameters or the classification results can not really describe the realization of business modeling. The artificial neural network is becoming a very popular alternative in prediction and classification task due to its associated memory characteristic and generalization capability. However, neural network has been criticized by its long training process in the application of classification problems. In order to solve the above-mentioned drawback, the proposed study trying to explore the performance of data classification by integrating the artificial neural networks technique with the linear discriminant analysis and fuzzy discriminant analysis approach respectively.
To demonstrate the inclusions of the classification results from the linear discriminant and fuzzy discriminant analysis would improve the classification accuracy of the designed neural networks, classification tasks are performed on two data sets, the often used Iris data and one practical bank credit card data. As the results reveal, the two proposed integrated approach provides a better initial solution and hence converges much faster than the conventional neural networks. Besides, in comparison with the traditional neural network approach, the classification accuracies increase for both cases in terms of the two proposed methodology. Moreover, the superiority of the proposed technique can be observed by comparing the classification results using only linear discriminant or fuzzy discrimintant analysis approaches.
目 錄
目錄………………………………………………………………Ⅰ
圖目錄……………………………………………………………Ⅲ
表目錄……………………………………………………………Ⅴ
第壹章 緒論……………………………………………………1
第貳章 文獻回顧………………………………………………5
2.1、資料探勘…………………………………………………5
2.2、鑑別分析…………………………………………………9
2.3、模糊鑑別分析……………………………………………10
2.4、類神經網路………………………………………………11
第參章 研究方法………………………………………………14
3.1、鑑別分析…………………………………………………14
3.2、模糊鑑別分析……………………………………………15
3.3、倒傳遞類神經網路………………………………………19
第肆章 資料分析………………………………………………23
4.1、演化式鑑別方式…………………………………………23
4.1.1、花卉資料…………………………………24
4.1.2、銀行資料……………………………………………32
4.2、演化式模糊方式……………………………40
4.2.1、花卉資料………………………41
4.2.2、銀行資料………………………49
第伍章 結論……………………………………………………58
參考文獻………………………………………………………61
圖 目 錄
圖1:知識發現之流程……………………………………… 6
圖2:資料探勘之研究架構…………………………………9
圖3:神經元之構造………………………………………12
圖4 倒傳遞類神經網路之構造……………………………19
圖5:{4-9-1}類神經網路模式(1)之訓練樣本RMSE趨勢圖
-花卉資料………………………………………………….27
圖6:{5-9-1}演化式鑑別方式之訓練樣本RMSE趨勢圖
-花卉資料………………………………………………….30
圖7:{6-12-1}類神經網路模式(1)之訓練樣本RMSE趨勢圖
-銀行資料…………………………………………………35
圖8:{7-16-1}演化式鑑別方式之訓練樣本RMSE趨勢圖
-銀行資料………………………………………………….38
圖9:{4-8-1}類神經網路模式(2)之訓練樣本RMSE趨勢圖
-花卉資料…………………………………………………..44
圖10:{5-11-1}演化式模糊方式之訓練樣本RMSE趨勢圖
-花卉資料………………………………………………….47
圖11:{4-11-1}類神經網路模式(2)之訓練樣本RMSE趨勢圖
-銀行資料………………………………………………….52
圖12:{5-15-1}演化式模糊方式之訓練樣本RMSE趨勢圖
-銀行資料………………………………………………….55
表 目 錄
表1:花卉資料之鑑別分析結果………………………………25
表2:類神經網路模式(1)不同參數組合之預測結果-花卉資料26
表3:類神經網路模式(1)之判別結果-花卉資料………………27
表4:演化式鑑別方式在不同參數組合之預測結果-花卉資料29
表5:演化式鑑別方式之判別結果-花卉資料….………………31
表6:花卉資料之三種模式判別結果比較表…………………… 31
表7:銀行資料之鑑別分析結果………………………………… 32
表8:類神經網路模式(1)不同參數組合之預測結果-銀行資料34
表9:類神經網路模式(1)之判別結果-銀行資料………………36
表10:演化式鑑別方式不同參數組合之預測結果-銀行資料… 37
表11:演化式鑑別方式之判別結果-銀行資料………………… 38
表12:銀行資料之三種模式判別結果比較表…………………39
表13:三種模式之第一類及第一類錯誤率比較表-銀行資料…40
表14:花卉資料之模糊鑑別分析結果…………………………42
表15:類神經網路模式(2)不同參數組合之預測結果-花卉資料43
表16:類神經網路模式(2)之判別結果-花卉資料……………44
表17:演化式模糊方式在不同參數組合之預測結果-花卉資料46
表18:演化式模糊方式判別結果-花卉資料..…………………48
表19:花卉資料之三種模式判別結果比較表…………………49
表20:銀行資料之模糊鑑別分析結果…………………………50
表21:類神經網路模式(2)不同參數組合之預測結果-銀行資料51
表22:類神經網路模式(2)之判別結果-銀行資料………………53
表23:演化式模糊方式不同參數組合之預測結果-銀行資料…54
表24:演化式模糊方式之判別結果-銀行資料…………………56
表25:銀行資料之三種模式判別結果比較表…………………57
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