因此，本論文對此種情況，利用關聯法則提出多層次排序(Multi-Ranking)分類器，定義關聯分類器的規則執行順序及規則修剪的方法。由實驗結果顯示，多層次排序分類器在預測未知分類能有好的準確率表現與執行效率。 Association rule is one of the .adopted techniques frequently in data mining, then integrating the association rules into a associative classifier for predicting that data are not classified.
The association rules will be sorted by the algorithm’s definition before executing the association rules. In general, between the rule and the rule execution order no longer will change successively when the association rules are sorted. Actually, after executing higher rank , the lower ranking and unexecuted rules will have different confidence from initial confidence in the remaining data. And the rules’ execution order and importance will be difference.
Therefore, we propose a new classifier named Multi-Ranking classifier in view of the situation, defining the rules of the associative classifier execution orders. Moreover, Multi-Ranking classifier have good accuracy and execution performance in the experiment.
