Adaptive Distance Metrics for Nearest Neighbour Classification based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{agapitos:2013:EuroGP,

• author = "Alexandros Agapitos and Michael O'Neill and Anthony Brabazon",
• title = "Adaptive Distance Metrics for Nearest Neighbour Classification based on Genetic Programming",
• booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013",
• year = "2013",
• month = "3-5 " # apr,
• editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu",
• series = "LNCS",
• volume = "7831",
• publisher = "Springer Verlag",
• address = "Vienna, Austria",
• pages = "1--12",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-37206-3",
• DOI = "doi:10.1007/978-3-642-37207-0_1",
• abstract = "Nearest Neighbour (NN) classification is a widely-used, effective method for both binary and multi-class problems. It relies on the assumption that class conditional probabilities are locally constant. However, this assumption becomes invalid in high dimensions, and severe bias can be introduced, which degrades the performance of the method. The employment of a locally adaptive distance metric becomes crucial in order to keep class conditional probabilities approximately uniform, whereby better classification performance can be attained. This paper presents a locally adaptive distance metric for NN classification based on a supervised learning algorithm (Genetic Programming) that learns a vector of feature weights for the features composing an instance query. Using a weighted Euclidean distance metric, this has the effect of adaptive neighbourhood shapes to query locations, stretching the neighbourhood along the directions for which the class conditional probabilities don't change much. Initial empirical results on a set of real-world classification datasets showed that the proposed method enhances the generalisation performance of standard NN algorithm, and that it is a competent method for pattern classification as compared to other learning algorithms.",
• notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013 and EvoApplications2013",

}

Genetic Programming entries for Alexandros Agapitos Michael O'Neill Anthony Brabazon

Citations