傳統的K-最近鄰(K-Nearest Neighbors,簡稱KNN)分類演算法是使用一個固定的K值,由最相近的K個鄰居中,投票決定受測資料應歸屬於哪一個類別。然而,相關研究顯示,變動的K值可改善KNN的分類效果。因此,本研究在KNN分類演算法中,加入Local KNN及Fuzzy C-means歸屬程度值的概念,讓個別測試資料使用較適合其本身的K值,進而改善整體分類效果。
The K-nearest-neighbor algorithm traditionally predicts the class of a record based on the decision from the K nearest neighbors of the record, for a fixed K value. However, recent studies showed that using different K values for different records could improve the prediction accuracy. This study integrates Fuzzy C-means algorithm to assist determining a proper K value for each record in a local KNN algorithm. Performance results show this method outperforms the traditional KNN in term of prediction accuracy.