在這資訊爆炸的時代，資訊的普及性提升，因此如何從有限的資料裡找尋找並歸納出我們想要且有用的資訊，是小樣本學習中非常重要的課題。近年來，對於整合法之研究主要琢磨於模式整合過程，對於如何整合其結果之研究相對甚少，而資料探勘的方法又可分為分類與預測，整合法在分類預測問題大多藉由投票多數決方式表決，在數值預測問題方面，最後整合階段則大多以簡單平均法為之，但簡單平均法較容易受極端值影響，尤其在樣本數較少的情況下更為明顯，故本研究考量此問題，針對單一模式整合法應用於數值預測問題時，提出一個基於模糊理論與盒鬚圖之整合權重計算方法，冀望能改善此問題。
本研究將拔靴整合法(Bootstrap Aggregating, Bagging)進行改良，運用支援向量迴歸作為預測模型，依據各預測模型的偏誤值來計算權重，因此每筆預測值都會得到一筆與其對應的權重值，最後再計算各模式於追求最小誤差目標下之妥協預測值，以取得系統之穩健性。在方法的效果驗證上，本研究與簡單平均法、模式預測值進行比較，同時以從面板廠所取得之實務個案，冀望能夠提升多模式整合法的預測準確率。

In the age of information explosion,it’s easier to reach out to information,so how to explore and conclude some useful information in limited data is a pretty important study in small data learning.nowadays,the studies in ensemble method mostly focus on the process instead of the result.the methods in datamining can be divided into classification and prediction.in ensemble method ,voting is the most common way to deal with classification,but in numerical prediction problem,average method is the most common way to calculate the result,but it can be easily affected by some extreme values,especially in the circumstances of small datasets
We make an improvement in Bagging.We use SVR as our prediction model ,and calculate the error value based on our prediction model,so we can get a corresponding weight value of each prediction value,and then we can calculate the compromise prediction value under the purpose of getting the smallest error value.Therefore,we can stabilize our system,and we compare our method to average method in order to examine the effect of our study,and we also take the practical case in panel factory to prove the improvement in single-model ensemble method

(一)中文部分
陳惠昭 (2014)。使用整體趨勢擴散技術提升多模式整合法之預測準確率。碩士論文，國立成功大學工業與資訊管理學系。
(二)英文部分
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