多項式簡易貝氏分類器因為其做法簡單、計算效率高以及良好的分類效能，被廣泛的運用於許多領域中。但這卻僅限於資料均勻分布的情況，多項式簡易貝氏分類器因為其本身的機制，導致在預測不平衡資料集的效能上與其他分類演算法相比顯得較差。而當不平衡資料伴隨高維度的特性時，類別不平衡問題將更加嚴重，形成難以處理的高維度不平衡資料集，造成分類器的預測效能不佳且效率低落。而過去處理類別不平衡問題時，大多以資料層面進行改善，但資料層面的方法可能會錯失潛在有用的資訊或更動原始資料的分配，進而產生無法預期的錯誤。因此，本研究將以演算法層面為出發點改善多項式簡易貝氏分類器於高維度不平衡資料集上的分類效能。會針對類別使用不同參數求取方法，於分類器中加入廣義狄式分配參數，並且利用修改分類器的學習機制，減弱分類結果中偏頗於多數類別的情況，以提升分類效能。實驗結果顯示，有效的使用參數求取方法和移除類別值機率方法，可以大幅提升MNB的整體分類情形。本研究所提出的分類器雖然運算時間較長，但利用特徵分組的方式加速求取參數，因此運算時間仍在合理的範圍內，並且分類器於各資料集中召回率和精確率皆表現良好，數值較為一致，在七個不平衡資料集的實驗中，共有六個分類結果中的F-measure是最優秀的，並取得最高的平均F-measure，與MNB、RIPPER和Random Forest相比擁有更優越的效能且表現更為穩定。

Multinomial naïve Bayesian classifier is widely used in many fields because of its high computational efficiency, and great classification performance. However, this is limited to the data set is normal distribution. When the data set is imbalanced, the number of positive instances is few. The probability estimates for calculating the classification probability of this class value can be unreliable in applying multinomial naïve Bayesian classifier. If the imbalanced data set is accompanied by high-dimensional characteristics, the problem of imbalanced classes will be more serious, resulting in poor classification performance and inefficiency. In past literature, sampling methods have been added to change the distribution of dataset. However, this method may lose potentially useful information or increase the computational burden of the algorithm. Therefore, in order to improve the classification performance of multinomial naïve Bayesian classifier on high-dimensional imbalanced data sets, the method of parameter setting for generalized Dirichlet priors and removing the probability for class have been added to adjust the algorithm of the classifier, expected to attenuate the bias in the classification results. For reducing the computation burden, feature grouping is also used in parameter setting. The experimental results show that those methods can greatly improve the classification performance of MNB. In the seven high-dimensional imbalanced dataset experiments, the modified classifier in this study has the best F-measure among the six classification results, and achieved the highest average F-measure. It shows that the modified classifier has superior and more stable performance than MNB, RIPPER and Random Forest.

姚靜姍，(2018)。簡易貝氏分類器在不平衡資料集上效能改善之研究。國立成功大學資訊管理研究所碩士班碩士論文。
黃于珊，(2009)。多項式簡易貝氏分類器中廣義狄氏分配之參數設定方法。國立成功大學資訊管理研究所碩士班碩士論文。
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