在真實生活中，很多案例都受限於樣本數的不足，造成資料結構不完整，資訊不明確，使得決策條件受限，因此如何從獲得之少量資料中，發掘有意義的資訊以建立可靠穩定的知識模式，進而獲得有效資訊，在近年來已是重要的議題。本研究基於事前知識，利用M5'模式樹的學習過程，提出一個虛擬樣本產生方法，首先利用M5'對資料分割的過程中，取得屬性的可能值域；再透過事前知識的方式，結合可能性評估機制產生虛擬樣本，本模式亦可同時處理數值與名目屬性。實驗結果顯示，透過此方法產生之虛擬樣本，在加入原小樣本後所建構之預測模式，確實較原小樣本對未知母體的預測誤差與準確率有明顯的改善。

This research aims to develop an effective procedure for leaning more knowledge from small datasets. In most small dataset learning tasks, however, owing to the incomplete data structure, the explicit information for decision makers is limited. The proposed procedure, based on the prior knowledge obtained by the M5’ model tree, generates more training samples to learn more information hidden inside the small datasets. In addition, the performance of five modeling tools, M5’, back-propagation neural network, support vector machine for regression, multiple linear regressions, and C4.5 decision tree is also improved. The proposed procedure can handle both numeric and nominal attributes, while fuzzy theory-based VSG algorithms cannot. Nine public datasets are taken to form six kind learning problems for performance evaluation; in addition, two real cases are taken to compare with Mega-Trend-Diffusion method which is the state of the art virtual sample generation algorithm. All the results show the significant improvements.

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