在眾多分類方法中，由於簡易貝氏分類器具有使用簡便、運算效能佳以及分類預測正確率高之優點，因此被廣泛地應用在許多分類任務上。然而許多分類方法都會有一個基本假設，就是資料的分布並非高度傾斜，因此當我們把分類方法應用於資料時，在大多數的情況下可以取得理想的結果，但是在一資料集當中，大多數的資料會集中在某一類別，而所關注的則是占少數資料的類別，就形成了所謂不平衡資料集。由於簡易貝氏分類器本身的學習機制是將類別值機率與所有屬性條件機率相乘，因此在不平衡資料集中，因兩類別的資料筆數相差甚遠，致使類別值機率差異較大，可能會導致簡易貝氏分類器在學習的過程中，誤將少數類別資料預測為多數類別，因此將測試類別值機率對簡易貝氏分類器的影響，此外，透過貝氏屬性挑選法對屬性做重要性排序之外，亦會做特徵選取，並導入先驗分配來調整屬性參數，以此提升簡易貝氏分類器的分類效能。從UCI資料存放站下載10個資料集，並將其處理成不平衡資料集來進行試驗，在實證結果中顯示考量類別值機率與否所造成的影響不大，但導入先驗分配可顯著提升簡易貝氏分類器在不平衡資料集上的效能，且此改善可使得簡易貝氏分類器與分類方法RIPPER相匹配，但是與Random Forest相比之下還是顯得稍為劣勢。

The number of positive instances is generally far larger than the number of negative instances in an imbalanced dataset. Since the number of positive instances is few, the probability estimates for calculating the classification probability of this class value can be unreliable in applying naïve Bayesian classifier. This could be the main reason for naïve Bayesian classifier to have a relatively poor performance on imbalanced data sets. This study first investigates whether the occurring probabilities of class values should be considered in calculating classification probabilities. Then attributes are ranked for introducing generalized Dirichlet priors to improve the performance of naïve Bayesian classifier on imbalanced data sets. The experimental results obtained from 10 data sets show that removing the occurring probabilities of class values in calculating classification probability is not necessary and that introducing priors for attributes can generally achieve a higher F-measure on imbalanced data sets. The naïve Bayesian classifier with priors can have a competitive performance with respective to RIPPER algorithm, while its F-measure is lower than that of Random Forest in the most imbalanced data sets.

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