OutRank 原是一種基於對像相似性所進行的異常偵測方法。不同於以距離或是密度來偵測的形式，OutRank 以計算資料點間的相似性，來找出在資料中的異常小族群。本論文延伸此概念，擴展應用到分類、監督式學習的問題上。根據 OutRank 的性質，我們可以得到各筆資料間的相似度，因此我們假設同一族群間的相似度會較接近。在本論文中，我們會針對不同的資料去做驗證，並且與經典的分類方法 : Random Forest 去做比較。

OutRank was originally developed as an anomaly detection method based on object similarity. Unlike distance or density-based detection approaches, OutRank calculates the similarity between data points to identify small anomaly groups within the data. This study extends the concept of OutRank and applies it to classification and supervised learning problems. Based on the nature of OutRank, we assume that the similarity between data points within the same group will be higher. In this study,we verify this assumption using different datasets and compare the results with the classic classification method, Random Forest.

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