Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier

In this work we introduce a new distance estimation technique by boosting and we apply it to the K-Nearest Neighbor Classifier (KNN). Instead of applying AdaBoost to a typical classification problem, we use it for learning a distance function and the resulting distance is used into K-NN. The proposed method (Boosted Distance with Nearest Neighbor) outperforms the AdaBoost classifier when the training set is small. It also outperforms the K-NN classifier used with several different distances and the distances obtained with other

• Type: Journal
• Year: 2006
• Where: PRL
• Authors: Jaume Amores, Nicu Sebe, Petia Radeva
• Keywords: Distance Estimation Technique, K-nearest Neighbor, PRL 2006, Typical Classification Problem