在過去數十年間，雖然有許多學習方法被開發以擷取資料的知識，但他們大多是基於訓練樣本可以完整呈現母體特性的假設前提下進行。如果訓練樣本無法完整表達母體特性時，此些方法所學習的知識對於決策者而言可能是不充足的，或甚至是偏誤的。因此針對小樣本的學習問題，本研究提出一個基於模糊理論的方法，藉由重建小樣本資料可能的樣本分配而產生新的訓練樣本以供演算法進行充分的學習。本方法包含一組新的值域估算函式以及一個樣本產生法。為了驗證方法之效果，本研究從一家在薄膜液晶顯示器產業中居於領導地位的公司內取得兩筆真實案例，採用倒傳導類神經網路和支持向量迴歸兩種學習演算法進行建模，此外並使用Bagging (bootstrap aggregating)和SMOTE (synthetic minority over-sampling technique)兩種樣本生成法進行效果比較。實驗結果顯示，當兩種學習演算法使用本研究所產生之新訓練樣本建模後，對於測試樣本的預測誤差，比使用Bagging與SMOTE所產生之新訓練樣本去建構之模型具有統計顯著性的低。

Over the past few decades, numerous learning algorithms have been proposed to extract knowledge from data. The majority of these algorithms have been developed with the assumption that training sets can denote populations. When the training sets contain only a few properties of their populations, the algorithms may extract minimal and/or biased knowledge for decision makers. This study develops a systematic procedure based on fuzzy theories to create new training sets by rebuilding the possible sample distributions, where the procedure contains new functions that estimate domains and a sample generating method. In this study, two real cases of a leading company in the thin film transistor liquid crystal display (TFT-LCD) industry are examined. Two learning algorithms, a back-propagation neural network and support vector regression, are employed for modeling, and two sample generation approaches, bootstrap aggregating (bagging) and the synthetic minority over-sampling technique (SMOTE), are employed to compare the accuracy of the models. The results indicate that the proposed method outperforms bagging and the SMOTE with the greatest amount of statistical support.

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