淡江大學機構典藏:Item 987654321/74556
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/74556


    Title: 關聯式分類演算法結合規則優先權以改善分類之準確度
    Other Titles: Improving the performance of associative classification algorithms with rule priorities
    Authors: 王務本;Wang, Wu-Pen
    Contributors: 淡江大學資訊工程學系碩士在職專班
    黃連進;Hwang, Lain-Jinn
    Keywords: 規則排序;規則相依性;關聯式法則;關聯式分類演算法;Associative Classification Algorithms;association rule;Ranking;Rule Dependency
    Date: 2011
    Issue Date: 2011-12-28 18:52:14 (UTC+8)
    Abstract: 雖然已有許多關聯式分類演算法被發表，但是都沒有將規則相依問題考慮進去。而規則相依問題會造成規則信賴度的改變甚至規則及類別的改變，進而影響到分類的結果，因此，要解決規則相依問題（找尋最佳規則執行順序）將是一個非常耗時的工作，本論文將提出Rule Priority演算法來對規則做排序，來達到較佳的執行順序，降低規則相依問題對分類結果產生的影響，進而改善最後分類的結果。因我們提出的演算法是一種時間多項式的演算法，所以可以很輕易的跟任何關聯式分類演算法結合。而在本論文中，我們將Lazy演算法加上規則優先權的概念，來與僅使用Lazy演算法的方式進行比較，而實驗結果也證明，規則相依性的確可以改善分類的精確度。
    Although different associative classification algorithms have been proposed, none of the available associative classification algorithms consider the rule dependence problem that directly influences the classification accuracy of associative classification algorithms. Since the finding of the optimal execution order of class association rules (CARs) is a combinational problem, instead of finding the optimal execution order of CARs, in this paper we propose polynomial time algorithms to re-rank the execution order of CARs by rules’ priority. This reduces the influence of rule dependency problems. Consequently, the performance (the classification accuracy and recall rate) of the associative classification algorithms can be improved. The experimental results show that using LAZY with our method can get better classification results than that of the LAZY association classifier without considering the rule dependence problem.
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Thesis

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