分類在資料探勘中一直是非常重要的一部份，透過資料所提供的資訊有助於人們預測未來可能發生的事件，因此不同的分類器都有其優勢去處理不同特性的分類問題；其中，單純貝氏分類器更是被大家廣為使用的分類器之一，其具有效率快且分類結果穩定等特性，因此在實務上也被廣泛使用。

本研究以貝氏分類器為基礎，透過決策樹來篩選出重要的屬性，接著利用決策樹的特性來幫忙決定屬性的初始權重(亦即其重要性)，然後再使用最小平均平方學習法的方式加以對權重做調整，訓練完成後將貝氏分類器賦予各屬性的權重，用其作為資料的分類和預測。實驗結果顯示針對各種不同性質(亦即不同領域)的資料集有很好的辨識能力，例如糖尿病資料集(醫療)、鳶尾花資料集(植物)、玻璃辨識資料集(物品)等等，並不侷限於某一個特定領域，所以本論文的分類模型具有較佳的強健性。

Classification is always an important part in data mining. The information of data can help us predicting what will happen in the future. Therefore, there are different merits for different classifiers to process various classified problems. Among many classifiers, Naive Bayes Classifier works more effectively and stably, so it is the most popular classifier in practical applications.

In this paper, firstly, the Decision Tree based on Bayes Classifier is used to decide the major attributes that are considered to give more contribution in terms of classification. Secondly, the initial weight for each of the selected major attributes is set up based on the nature of Decision Tree in which the level distance from the tree root reflects the role of importance to the classification. Thirdly, the weight of each selected major features is then adjusted by the least mean square learning process. Finally, the resultant weights of major features from the learning are used in the Bayes Classifier for classification and prediction. The experiment shows that the recognizing ability of the classifier proposed in this thesis is relatively better against other some compared approaches for various Datasets in the different domains. The experimental result also shows the proposed approach has better robustness.

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