決策樹形式知識整合之研究

The Research on Decision-Tree-Based Knowledge Integration

馬芳資(Fang-Tz Ma) ； 林我聰(Woo-Tsong Lin)

《資訊管理學報》 12卷3期 (2005/07) Pp. 247-279

https://doi.org/10.6382/JIM.200507.0247

摘要

隨著知識經濟時代的來臨，掌握知識可幫助組織提昇其競爭力，因此對於知識的產生、儲存、應用和整合，已成為熱烈討論的議題，本研究擬針對知識整合此議題進行探討。在知識呈現方式中，決策樹(Decision Tree)形式知識為樹狀結構，可以用圖形化的方式來呈現，它的結構簡單且易於瞭解，本研究將針對決策樹形式知識來探討其知識整合的課題。本研究提出一個合併選擇決策樹方法MODT(Merging Optional Decision Tree)，主要是在原始決策樹結構中增加一個選擇連結(Option Link)，來結合具有相同祖先(Ancestor)的兩個子樹；而結合的方式是以兩兩合併的方式，由上而下前序式比對兩棵決策樹的節點(Node)，利用接枝(Grafting)技術來結合兩探樹的知識。再者，利用強態法則(Strong Pattern Rule)概念來提昇合併決策樹(Merged Decision Tree)的預測能力。本研究利用實際信用卡客戶的信用資料來進行驗證，以隨機抽取的五組獨立測試例子集來測試二十棵原始決策樹(Primitive Decision Tree)和十棵合併決策樹，並比較兩者的預測準確度，是故總共進行五十次的比較，合併決策樹的準確度同時大於、等於兩棵原始決策樹的比例為79.5%；並且針對兩者的準確度進行統計檢定，我們發現合併決策樹的準確度是有顯著大於原始決策樹。亦即本研究所提出之合併選擇決策樹方法可達成知識整合與累積的目的。

關鍵字

決策樹 ； 決策樹合併 ； 知識整合

並列摘要

Along with the approach of the knowledge economy era, mastering knowledge can aid organizations to improve their competitive abilities. Therefore, knowledge creation, retention, application, and integration are becoming the hottest themes for discussion nowadays. This research tends to focus on discussion of knowledge integration and related subjects. Among the methods of knowledge representation, the decision tree is the most common. It shows knowledge structure in a tree-shaped graphic. Decision trees are considerably simple and easily understood; thus we focus on decision-tree-based knowledge in connection with the theme of knowledge integration. Our research proposes a method called MODT (Merging Optional Decision Tree), which merges two knowledge trees at once and adds an optional link to merge nodes which have the same ancestor. In MODT, we compared the corresponding nodes of two trees by using the top-down traversal method to make sure their nodes were the same. When their nodes were the same, we recounted the number of samples and recalculated the degree of purity. When their nodes were not the same, we added the node of the second tree and its descendants to the first tree by using the Grafting Technique. This yielded a completely merged decision tree. The Strong Pattern Rule was used to strengthen the forecast accuracy when using merged decision trees. We took sample data from credit card users to carry out the experiment, and five groups of the test samples were extracted randomly to test twenty primitive and ten merged trees. Eventually, after fifty comparison tests, the merged tree showed a 79.5% chance of being equal or more accurate than the primitive trees. This research result supports our proposition that the merged decision tree method could achieve a better outcome with regard to knowledge integration and accumulation.

並列關鍵字

Decision Tree ； Decision Tree Merging ； Knowledge Integration

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