In order to improve the computational efficiency of conformal predictor, distance metric learning methods were used in the algorithm. The process of learning was divided into two stages: offline learning and online learning. Firstly, part of the training data was used in distance metric learning to get a space transformation matrix in the offline learning stage; Secondly, standard CP-KNN was conducted on the remaining training data with a nonconformity measure function defined by K nearest neighbors classifier in the transformed space. Experimental results on three UCI datasets demonstrate the efficiency of the new algorithm. © 2012 IFIP International Federation for Information Processing.
CITATION STYLE
Yang, F., Chen, Z., Shao, G., & Wang, H. (2012). Distance metric learning-based conformal predictor. In IFIP Advances in Information and Communication Technology (Vol. 382 AICT, pp. 254–259). Springer New York LLC. https://doi.org/10.1007/978-3-642-33412-2_26
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