Inproceedings,

Learning with missing data using Genetic Programming

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The 1st Online Workshop on Soft Computing (WSC1), http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/, Nagoya University, Japan, (19--30 August 1996)

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.

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