標題: 基因演算法在知識庫整合之應用
Using Genetic Algorithm for Integrating Multiple Rule-Sets
作者: 廖志茂
Chih-Mao Liao
曾憲雄
Shian-Shyong Tseng
資訊科學與工程研究所
關鍵字: 基因演算法; 專家系統; 診斷腦瘤;genetic algorithm; expert system ; diagnosing brain tumor
公開日期: 1994
摘要: 知識整合是發展一套專家系統時相當重要的技術,但是他有時花費不少時
間。知識整合的過程也往往相當令人厭煩,特別是整合從多個相同領域的
專家擷取而來的或各種不同學習演算法歸納而來的規則集知識庫。在本篇
文章中,我們提出一種自動整合規則集知識庫的整合方法。在我們的自動
整合方法裡,分成兩個發展的皆段,分別是規則集知識庫的編碼和整合。
知識庫的編碼階段中,我們將每個規則集知識庫編成以二進位表示的零壹
字串,此後再拿去當做整合階段中的初始成員。在整合階段裡,我們採用
基因演算法技巧,把多個知識庫整合,規納和尋找出最佳的知識庫。在此
同時,我們採用診斷腦瘤的專家領域來測試我們的方法,並且比較整合前
後的準確率高低及控制整合後的知識庫複雜度。實驗的結果顯示出,我們
的方法能獲得較高的準確率且整所花費的時間明顯的較低。
Knowledge-integration is a very important technique in
developing the expert system, but it sometimes takes much time.
Especially, when the multiple rule-sets are constructed by
multiple experts or induced by various learning algorithms, the
integrating process is tedious. In this paper, we will propose
an automated knowledge-integration approach to integrate
multiple rule-sets. Our approach consists of two phases: rule-
sets encoding and rule-sets integrating. For the encoding
phase, each rule-set is encoded to a bit-string as a member of
an initial population. For the integrating phase, an adaptive
searching technique (genetic algorithm) is used to induce the
optimal concept description from the multiple rule-sets. In the
mean time, experiments in diagnosing brain tumor (DBT) are
schemed to compare the accuracy of knowledge integration with
that of the original rule sets. Experimental results show that
the accuracy concept description can be obtained from the above
mentioned approach and the time consumption of the integrating
process is obviously reduced.
URI: http://140.113.39.130/cdrfb3/record/nctu/#NT830394056
http://hdl.handle.net/11536/59080
顯示於類別:畢業論文