Accuracy and Specificity Trade-off in \(k\)-nearest Neighbors Classification
The \(k\)-NN rule is a simple, flexible and widely used non-parametric decision method, also connected to many problems in image classification and retrieval such as annotation and content-based search. As the number of classes increases and finer classification is considered (e.g. specific dog breed), high accuracy is often not possible in such challenging conditions, resulting in a system that will often suggest a wrong label. However, predicting a broader concept (e.g. dog) is much more reliable, and still useful in practice. Thus, sacrificing certain specificity for a more secure prediction is often desirable. This problem has been recently posed in terms of accuracy-specificity trade-off. In this paper we study the accuracy-specificity trade-off in \(k\)-NN classification, evaluating the impact of related techniques (posterior probability estimation and metric learning). Experimental results show that a proper combination of \(k\)-NN and metric learning can be very effective and obtain good performance.
KeywordsFeature Space Leaf Node Semantic Similarity Information Gain Pairwise Constraint
This work was supported in part by the National Natural Science Foundation of China: 61322212, 61035001 and 61350110237, in part by the Key Technologies R&D Program of China: 2012BAH18B02, in part by National Hi-Tech Development Program (863 Program) of China: 2014AA015202, and in part by the Chinese Academy of Sciences Fellowships for Young International Scientists: 2011Y1GB05.
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