- 學位論文
以模糊決策樹與適應學習網為基礎之知識探索模型
A Fuzzy Knowledge Discovery Model Using Fuzzy Decision Tree and Fuzzy Adaptive Learning Control Network
摘要
探索複雜資料庫中所潛藏卻有用之企業訊息與作業經驗，十分複雜且具挑戰性。大型資料庫中之屬性個數眾多，管理決策者要在眾多的屬性中找出重要影響因子與答案之間的顯著關係十分困難；且資料庫中的資料多為數值形式，許多「因果關係」與「企業規則」卻為模糊形式。本研究旨在發展模糊規則學習系統，自數值資料庫中發掘出潛藏之企業訊息與作業經驗。本研究首先提出以「Dodgson’s function」與「百分位數統計量」界定歸屬函數之模糊集合範圍，並提出以「模糊決策樹」與「模糊適應學習控制網路」為基礎之知識探勘模型，據以產生具體的知識規則。本研究首先將資料庫中之數值屬性予以模糊化，分別引用決策樹分類演算法建立模糊決策樹，與倒傳遞類神經網建立模糊適應學習控制網路，並發展顯著性檢定與刪減更新權重連結，精練知識規則。本研究以「半導體測試作業之延遲時間預測」與「高等教育通識課程成績預測」之資料庫為對象進行研究。實驗結果發現，「模糊決策樹」與「模糊適應學習控制網路」構建規則之模型，可產生精簡之模糊規則庫，準確度亦高。
並列摘要
To explore business information and operation experience from relational databases is a challenge, because many cause-effect relationships and business rules are fuzzy. It is therefore difficult for a decision-maker to discover important factors. At first, we defined fuzzy sets of the membership functions by Dodgson’s function and quartile statistic. Next, we developed a data-mining model base on both the fuzzy decision tree and fuzzy adaptive learning control network—these two concepts help generate concrete rules. This research adopted a decision-tree based learning algorithm and back-propagation neuro network to develop a fuzzy decision tree and fuzzy adaptive learning control network. In order to refine rules, we took the advantage of the Chi-square test of homogeneity to reduce the connection of weight. This research also used the Prediction of Tardiness in Semi-conductor Testing and the Prediction of Grades by an Advanced General Knowledge Course as samples. The results showed that the two models generated a compact fuzzy rule-base that yielded high accuracy.