Support and Confidence Based Rule Extraction Method for Neural Networks

撐度和信度為基的類神經網路法則萃取法

10.29977/JCIIE.200605.0007

楊烽正(Feng-Cheng Yang)

競爭式學習神經網路 ； 一致性 ； 撐度 ； 信度 ； 關聯法則 ； 約略集合 ； competitive learning neural network ； consistency ； support ； association rule ； rough set

工業工程學刊

23卷3期（2006 / 05 / 01）

233 - 244

英文

本文提出一種新的競爭式學習類神經網路法則萃取法以應用於資料分類問題。首先，使用一個以距離衡量屬性值變異的熵值求算為評量的屬性離散化演算法進行屬性離散化。透過約略集合的一致性求算，評估是否持續進行區間合併以獲得數量較少且具有適當一致性的鄰接區間。其次，避免使用冗雜無效率的組合排列法，由低維度法則的建立開始，逐步建構具有信度和撐度的法則。本文引用並修改關聯法則分析技術中的信度和撐度計算方式，以適用於逐步建構的資料分類法則。最後並以法則歸併演算法，合併法則和精減法則數，以獲得效能較佳但分類功能相同的法則庫。本文並進行五組標竿問題的數據測試和分類正確性比較。結果顯示本研究提示的法則萃取法建構出的法則庫的資料分類正確率皆勝過C4.5決策樹演算法。數據顯示經本法產生的法則庫，在不同資料組合中皆有高度的正確性和強韌性。

This paper presents a rule extraction method for competitive learning neural networks that are used for data clustering. First, a partition algorithm is used to divide attribute values into non-overlapped intervals. Consistency evaluation method adopted from rough set theory is used to partition attribute values. The generation of the set of adjoined intervals is controlled by the consistency evaluation against with the data distribution on the neural networks. By keeping the level of consistency, the set of adjoined intervals correctly reflects the data distribution on the networks. Second, instead of exhaustively traversing all combinations of the intervals to test possible rules, our method constructs the rules systematically and recursively from lower dimensions to higher ones. Using and adapting the techniques of evaluating amounts of support and confidence for an association rule, the constructed rules from our method are supported by the data clustering to the networks with adequate confidence. Finally, a rule reduction and merging algorithm is used to obtain a concise yet accurate set of rules. To verify the correctness of the constructed rules from our method, five benchmark problems are tested and results are compared. Comparison shows that the correctness of the rules generated from our method is more accurate than those from decision tree C4.5.

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