核依賴估計是一個計算兩個抽象物件之間的依賴的學習架構。雖然已經有很多方面的應用，但是它的一些特性還沒有被徹底的研究。本文討論了核依賴估計的兩個實際操作中常見的問題。第一個問題是它對每個標籤的實數值輸出與最終目標所希望求得的二進位值還是有所區別的。通常解決這個問題的做法是使用特定的臨界值策略。本文提出一個替代方法，通過特殊的堆疊歸納法，加入了一個第二層的分類器。第二個問題是關於核依賴估計應用於不平衡數據集時性能的衰減現象。我們的實驗結果顯示核依賴估計並不直接適用於不平衡數據集，對此我們提出了補救措施來處理不平衡數據集。 Kernel dependency estimation is a learning framework of finding the dependencies between two general classes of objects. Although already succeeded in many kinds of applications, its properties are not fully studied. In this paper we will discuss two practical issues in it. The first one is about its real-value output for each label which is different from the ultimate target-binary value for one of k coding scheme. Thus there usually exists a gap between predicted real-value from KDE and the ground true binary value. One common practice to reduce the gap is using threshold strategies. In this paper we provide an alternative approach to combine a second level classifier by a special degenerated form of stacked generalization. The second issue is about how the performance decreases when KDE is applied to classification with skewed data, our experiments show KDE is not an appropriate approach for skewed data, and then we provide a remedy to handle the skewed data.
