Classification algorithms
The multi-class metric problem in nearest neighbour discrimination rules
Pattern Recognition
C4.5: programs for machine learning
C4.5: programs for machine learning
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 2002 ACM symposium on Applied computing
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
k-nearest Neighbor Classification on Spatial Data Streams Using P-trees
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
K nearest neighbor search in navigation systems
Mobile Information Systems
Data mining from 1994 to 2004: an application-orientated review
International Journal of Business Intelligence and Data Mining
International Journal of Business Intelligence and Data Mining
Vietnamese Knowledge Base development and exploitation
International Journal of Business Intelligence and Data Mining
Using multi decision tree technique to improving decision tree classifier
International Journal of Business Intelligence and Data Mining
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The k-nearest neighbour (KNN) technique is a simple yet effective method for classification. In this paper, we propose an efficient weighted nearest neighbour classification algorithm, called PINE, using vertical data representation. A metric called HOBBit is used as the distance metric. The PINE algorithm applies a Gaussian podium function to set weights to different neighbours. We compare PINE with classical KNN methods using horizontal and vertical representation with different distance metrics. The experimental results show that PINE outperforms other KNN methods in terms of classification accuracy and running time.