由於電腦資訊和科技網路的磅礡發展，形成了現今的大數據時代，而企業為了能夠長久的發展，經常透過獲取大量的數據資料來建構出機器學習的模型來發展該企業之未來策略，但對於大數據而言，常常會產生不平衡資料集(imbalanced data set)的問題，因為現實的世界中樣本的分配常常是不均等的，對於傳統的機器學習分類演算法來說，少數的樣本類別常常會被歸類為誤差，會使得模型傾向多數類別，而導致分類模型沒辦法有效的將少數類別樣本區別開來，進而導致決策的失誤，所以目前如何將不平衡資料集建構出一個可靠的學習模型，對學術界和科技界會是個重要的挑戰，因此為了增加少數類別之樣本資訊量，本研究提出新的增加過抽樣(oversampling)方法來增加少數類別之樣本數量，而提取的少數類別樣本取自模擬資料集來建構出支援向量機(Support Vector Machine；SVM)模型之中的超平面(hyperplane)上，爾後使用整體趨勢擴散(Mega-Trend-diffusion；MTD)技術來生成出虛擬樣本，並應用我們研究的新型評估機制，此機制是藉由孿生神經(Siamese Network)為基礎所創造的，主要用來來評估生成的虛擬樣本是否符合我們的要求，最後使用該模擬資料集建構出的預測模型並對實際數據進行評估，而評估分類指標將由Geometric Mean (G-mean)、F-measure (F1)、AUC、ACC來評測標準和判斷本研究方法不平衡資料集的學習效率。

Due to the majestic development of computer information and network technology, the present era of big data has been formed, and enterprises often construct machine learning models to develop their strategies by acquiring a large amount of data. For traditional machine learning classification algorithms, the minority class was often classified as an error, which made the model trend toward the majority class, and the classified model cannot effectively distinguish the minority samples, which lead to erratic decision-making. Therefore, to increase the amount of sample information on the minority class, this paper developed a new method of oversampling to increase the samples of the minority class, and the extracted samples of the minority class were taken from the support vector which was found from the hyper plane of the support vector machine. The virtual samples are generated by the Mega-Trend-diffusion (MTD) technique, and the evaluation mechanism of the virtual samples based on the Siamese Network was created to evaluate whether the generated virtual samples were suitable for our requirements. In the end, the prediction model was constructed using the simulated dataset and run on the actual data, and the evaluation of learning model efficiency in this research was used by Geometric Mean (G-mean), F-measure (F1), ACC, AUC, AVE.

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