NWFE演算法原用於小樣本高維度之資料轉換以改善分類效果，本文除指出該轉換 法在大樣本低維度之分類資料，同樣可得改善分類效果外。並提出新穎改進之「基於最遠 參照點之無參數加權特徵萃取轉換演算法」，簡記爲「Liu轉換法」，以SVM分類演算法 爲例，經以大樣本低維度實際資料，採五折及去一交叉驗證法，進行實驗比較，結果顯示 經NWFE資料轉換之SVM分類效果顯著改善，而經Liu轉換之SVM分類演算法有更佳 分類表現。

The NWFE-Algorithm is originally used to improve the accuracy of a classifier for the small sample data with higher dimension, this paper pointed out that the above algorithm also can be used to improve the accuracy of a classifier for the large sample data with lower dimension. Furthermore, in this paper, a novel separable transformation algorithm based on the outmost points denoted Liu-Transformation is proposed. For evaluating the performances of the SVM without any transformation, the SVM with the NWFE-Transformation and the SVM with the Liu-Transformation, a real data experiment by using -fold and Leave-one-out Cross-Validation accuracy is conducted. Experimental result shows that the SVM with the NWFE-Transformation is better than the SVM without any transformation, and the SVM with the proposed Liu- Transformation algorithm has the best performance.