在文件分類(text classification)之中，字庫法(bag-of-words representation)為最常被用來表達每一篇文章的表示法；而這表示法也導致了文件分類最主要的特性之一:資料的微度相當高也很稀疏。如何減少資料的維度使得學習演算法能有更佳的表現仍然是一個進行中的研究。一般最常使用的兩種從不同角度來減少資料維度的方法為:特徵選取(feature selection)，以及特徵萃取(feature extraction)。特徵萃取法是指從原本的資料的維度中尋找一個子空間，並且將原本的資料投影在這子空間上；特徵選取法則是依據一個給定的特徵評分方式來對特徵排名並且做選取的動作。在這篇論文中，我們的”輪循式字庫建立法”(round robin bag-of-words generation)則是結合了特徵選取以及特徵萃取的概念。”輪循式字庫建立法”主要可以分為兩個階段。在第一階段中，我們利用了文件分類中常用的潛在語義標引法(latent semantic indexing)來萃取每一個類別中的主要概念，再依據每個特徵再形成這條概念時對應的係數來對特徵做排序的動作；在第二階段中，我們引進了輪循式的概念對每類排序好的特徵做選取的動作。實驗的結果顯示出，我們的方法比互訊息(mutual information)以及卡方測試這兩個傳統的特徵評估方式，以及與使用全部的特徵在四個標準的文件資料集中表現的要好。我們更進一步的將輪循式的字庫建立概念引入戶訊息以及卡方測試中。實驗的結果展示了我們的方法可以與其他傳統的特徵評估法結合，並且在不同的資料中展現出不同的優勢。

In text classification, bag-of-words representation is a widely used method to represent documents, and it leads to a major characteristic: the high dimensionality of the sparse data. How to reduce the dimension of data efficiently to achieve a better performance in a learning task is an on-going research. There are two different approaches, feature extraction and feature selection, to reduce the data dimension. Feature extraction (dimension reduction) algorithms are finding an efficient subspace of the original space and then projecting the data on the subspace; feature selection methods are selecting informative features according to different feature evaluation criteria. In this thesis, we propose the "Round Robin Bag-of-words Generation (RRBWG)" for text classification which combines the ideas of dimension reduction and feature selection. The RRBWG method contains two stages. In the first stage, we extract the main concept of each category by the famous dimension reduction method, Latent Semantic Indexing (LSI); then we rank features by corresponding coefficient in extracted concepts for each category, since these concepts are the linear combination of the features. In the second stage, we select resulting ranked features in the first stage from each category by round robin selecting strategy to generate the bag-of-words. Comparison results show that RRBWG performs better than classical feature selection method with two feature scoring criteria, MI and chi-square-test and without feature selection methods consistently in four benchmark text collections. Furthermore, we introduce RRBWG into these two feature scoring criteria. Experimental results show that RRBWG can cooperate with various feature scoring criteria with different strength on benchmark datasets.

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