Abstract
The use of machine learning techniques to automatically analyze data for information is becoming increasingly widespread. In this chapter we examine the use of Genetic Programming and a Genetic Algorithm to pre-process data before it is classified using the C4.5 decision tree learning algorithm. Genetic Programming is used to construct new features from those available in the data, a potentially significant process for data mining since it gives consideration to hidden relationships between features. A Genetic Algorithm is used to determine which set of features is the most predictive. Using ten well-known data sets we show that our approach, in comparison to C4.5 alone, provides marked improvement in a number of cases.
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Smith, M.G., Bull, L. (2005). GAP: Constructing and Selecting Features with Evolutionary Computing. In: Ghosh, A., Jain, L.C. (eds) Evolutionary Computation in Data Mining. Studies in Fuzziness and Soft Computing, vol 163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32358-9_3
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DOI: https://doi.org/10.1007/3-540-32358-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22370-2
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