Advanced metrics for class-driven similarity search

This paper presents two metrics for the nearest neighbor classifier that share the property of being adapted, i.e. learned, on a set of data. Both metrics can be used for similarity search when the retrieval critically depends on a symbolic target feature. The first one is called local asymmetrically weighted similarity metric (LASM), and it exploits reinforcement learning techniques for the computation of asymmetric weights. Experiments on benchmark datasets show that LASM maintains good accuracy and achieves high compression rates outperforming competitor editing techniques like condensed nearest neighbor. The second metric, called the minimum risk metric (MRM), is based on probability estimates. MRM can be implemented using different probability estimates and performs comparably to the Bayes classifier based on the same estimates. Both LASM and MRM outperform the NN classifier with the Euclidean metric