Learning with missing data using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{backer:1996:WSC,

• author = "Gerriet Backer",
• title = "Learning with missing data using Genetic Programming",
• booktitle = "The 1st Online Workshop on Soft Computing (WSC1)",
• year = "1996",
• broken = "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/",
• month = "19--30 " # aug,
• organisation = "Research Group on ECOmp of the Society of Fuzzy Theory and Systems (SOFT)",
• publisher = "Nagoya University, Japan",
• keywords = "genetic algorithms, genetic programming, Machine learning, Missing data, Strongly Typed Genetic Programming, STGP",
• broken = "http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/d041.html",
• URL = "http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/files/backer.ps.gz",
• abstract = "Learning with imprecise or missing data has been a major challenge for machine learning. A new approach using Strongly Typed Genetic Programming is proposed here, which uses extra computations based on other input data to approximate the missing values. It eliminates the need for pre-processing and makes use of correlations between the input data. The decision process itself and the handling of unknown data can be extracted from the resulting program for an analysis afterwards. Comparing it to an alternative approach on a simple example shows the usefulness of this approach.",
• size = "5 pages",
• notes = "Adds {"}unknown{"} data type to STGP. demo on iris classification problem (see discussion on WSC1 pages) email WSC1 organisers wsc@bioele.nuee.nagoya-u.ac.jp",

}

Genetic Programming entries for Gerriet Backer

Citations