在過往小樣本學習方法中，虛擬樣本產生法已被證實能有效提升機械學習演算法的學習結果。然而在過往的虛擬樣本產生方法中，對於如何管控所產生之虛擬樣本的品質並未有明確的定義，如Huang and Moraga (2004) 所提出的擴散神經網路雖有考量屬性相關性於樣本生成過程，然而該方法卻僅侷限於屬性間的相關性必須大於0.9時，而如 Li et al. (2007) 所提出的整體趨勢擴散 (mega-trend-diffusion, MTD) 技術則是在假設屬性間獨立情況下，於產生樣本過程並未考量此間關聯性，而導致所產生之虛擬樣本超出母體範圍。雖小樣本資訊不足盡信，然小樣本資料本屬母體之部分集合，若能因而據此而生成保有小樣本特性之虛擬樣本，則可確保其合理性。因此本研究依此概念提出一套虛擬樣本篩選機制，藉由小樣本屬性間的關聯性來進行樣本篩選。本研究以MTD為例，於實驗結果顯示，經篩選過後的虛擬樣本除能保有小樣本之資訊外，於使用多元線性迴歸、以及倒傳遞類神經網路建模後，對於預測準確度能有更佳的改善。

Past small sample learning methods, utilizing virtual sample generation (VSG) methods has been shown to effectively enhance the outcomes of machine learning algorithms. However, most of the VSG methods do not have a clear definition on how to control the quality of the generated virtual samples; such as the diffusion neural network (DNN) proposed by Huang and Moraga (2004). Although the considerations are related to the sample generation process, DNN is limited in the correlation between attributes which must be greater than 0.9. In addition, the mega-trend-diffusion (MTD) technique proposed by LI et al. (2007) assumes the relationship between attributes is independent, i.e. the correlations are not considered in the sample generation process. This assumption would thus lead the generated samples to excess scopes of the populations. Although the information abstracted from small datasets is not convincible, the small datasets are parts of the populations. As well as the characters of the small datasets are reserved in the virtual samples, the rationality of them still can be ensured. Therefore, this study proposes a virtual sample sieving mechanism, with a small sample correlation between attributes for sample selection. The experimental results show that the characters of small datasets can be reserved when the virtual samples created by MTD are sieved by the proposed mechanism. In addition, the prediction accuracy of the multiple linear regressions and the back-propagation neural networks can further be improved.

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