- author = "R. Curry and M. I. Heywood",
- title = "One-Class Learning with Multi-Objective Genetic Programming",
- booktitle = "Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics",
- year = "2007",
- pages = "1938--1945",
- address = "Montreal",
- month = "7-10 " # oct,
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, evolutionary multi-criteria optimisation, one-class learning",
- ISBN = "1-4244-0991-8",
-
URL = "
http://users.cs.dal.ca/~mheywood/X-files/GradPubs.html#curry",
- abstract = "One-class classification naturally only provides one class of exemplars on which to construct the classification model. In this work, multi-objective genetic programming (GP) allows the one-class learning problem to be decomposed by multiple GP classifiers, each attempting to identify only a subset of the target data to classify. In order for GP to identify appropriate subsets of the one-class data, artificial outclass data is generated in and around the provided inclass data. A local Gaussian wrapper is employed where this reinforces a novelty detection as opposed to a discrimination approach to classification. Furthermore, a hierarchical subset selection strategy is used to deal with the necessarily large number of generated outclass exemplars. The proposed approach is demonstrated on three one-class classification datasets and was found to be competitive with a one-class SVM classifier and a binary SVM classifier.",
-
notes = "http://www.smc2007.org/program.html
rcurry_SMC07.pdf is twenty pages",
-
URL = "
http://users.cs.dal.ca/~mheywood/X-files/Publications/rcurry_SMC07.pdf",
