Otero, FEB and Silva, M.M.S and Freitas, A.A. and NIevola, J.C. (2003) Genetic Programming for Attribute Construction in Data Mining. In: Ryan, C. and Keijzer, M. and Poli, R. and Soule, T. and Tsang, E. and Costa, E., eds. Lecture Notes In Computer Science. Lecture Notes in Computer Science, 2610. Springer-Verlag pp. 384-393. ISBN 3-540-00971-X..
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For a given data set, its set of attributes defines its data space representation. The quality of a data space representation is one of the most important factors influencing the performance of a data mining algorithm. The attributes defining the data space can be inadequate, making it difficult to discover high-quality knowledge. In order to solve this problem, this paper proposes a Genetic Programming algorithm developed for attribute construction. This algorithm constructs new attributes out of the original attributes of the data set, performing an important preprocessing step for the subsequent application of a data mining algorithm.
|Item Type:||Conference or workshop item (UNSPECIFIED)|
|Uncontrolled keywords:||genetic programming, data mining, classification|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group|
|Depositing User:||Mark Wheadon|
|Date Deposited:||24 Nov 2008 18:01|
|Last Modified:||21 May 2011 23:47|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/13990 (The current URI for this page, for reference purposes)|
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