Evolving a Locally Optimized Instance Based Learner

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

@InProceedings{DBLP:conf/dmin/JohanssonKN08,

• author = "Ulf Johansson and Rikard Konig and Lars Niklasson",
• title = "Evolving a Locally Optimized Instance Based Learner",
• booktitle = "The 2008 International Conference on Data Mining",
• year = "2008",
• pages = "124--129",
• address = "Las Vegas, USA",
• month = jul # " 14-17",
• publisher = "CSREA Press",
• keywords = "genetic algorithms, genetic programming, instance-based learner, kNN, classification",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.1",
• URL = "http://bada.hb.se:80/bitstream/2320/4208/2/Johansson%2C%20K%C3%B6nig%2C%20Niklasson%20-%202008%20-%20Evolving%20a%20Locally%20Optimized%20Instance%20Based%20Learner.pdf",
• annote = "The Pennsylvania State University CiteSeerX Archives",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• language = "en",
• oai = "oai:CiteSeerX.psu:10.1.1.1011.1",
• abstract = "Standard kNN suffers from two major deficiencies, both related to the parameter k. First of all, it is well-known that the parameter value k is not only extremely important for the performance, but also very hard to estimate beforehand. In addition, the fact that k is a global constant, totally independent of the particular region in which an instance to be classified falls, makes standard kNN quite blunt. In this paper, we introduce a novel instance-based learner, specifically designed to avoid the two drawbacks mentioned above. The suggested technique, named G-kNN, optimises the number of neighbours to consider for each specific test instance, based on its position in input space; i.e. the algorithm uses several, locally optimised k, instead of just one global. More specifically, G-kNN uses genetic programming to build decision trees, partitioning the input space in regions, where each leaf node (region) contains a kNN classifier with a locally optimised k. In the experimentation, using 27 datasets from the UCI repository, the basic version of G-kNN is shown to significantly outperform standard kNN, with respect to accuracy. Although not evaluated in this study, it should be noted that the flexibility of genetic programming makes sophisticated extensions, like weighted voting and axes scaling, fairly straightforward.",
• notes = "http://www.dmin-2008.com/programme.htm",

}

Genetic Programming entries for Ulf Johansson Rikard Konig Lars Niklasson

Citations