「簡單貝氏分類法」是一種透過各特徵彼此間互相獨立下運用貝氏定理的分類器，現實生活中大部分的資料無法滿足任何兩個特徵皆不相關這項假設。
我們提出一種結合主成分分析與費雪資訊之簡單貝氏分類法，先利用「主成分分析」將各特徵轉化成任何兩個特徵皆不相關的新的特徵，並將各屬性欄位與類別欄位進行費雪資訊的計算，逐一選出具有較多資訊量的屬性，最後估計貝氏分類法所需的相似度，並將物件歸類於最高「事後機率」的類別。
我們以網路常用的標準資料集為研究與效能評估，探討利用主成分分析和費雪資訊降低特徵維度，此分類法準確率的變化。

Naive Bayes classifier is a simple probabilistic classifier which is based on applying Bayes’theorem which strong independence assumptions between the features.
We propose a method based on Naive Bayes classifier with Principal Components Analysis(PCA) and Fisher Information.
We use Principal Components Analysis to make features uncorrelated.
The transformed features are ranked by Fisher Information score which measuring the amount of information and calculate the posterior probability where the likelihood is replaced by p-value.
We conclude our research through the classification accuracy with some examples and present our vision for future research.

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