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Data preprocessing in predictive data mining

Published online by Cambridge University Press:  09 January 2019

Stamatios-Aggelos N. Alexandropoulos Open the ORCID record for Stamatios-Aggelos N. Alexandropoulos [Opens in a new window] ,
Sotiris B. Kotsiantis  and
Michael N. Vrahatis
Stamatios-Aggelos N. Alexandropoulos
Affiliation:
Computational Intelligence Laboratory (CILab), Department of Mathematics, University of Patras, GR-26110 Patras, Greece e-mail: alekst@math.upatras.gr, sotos@math.upatras.gr, vrahatis@math.upatras.gr
Sotiris B. Kotsiantis
Affiliation:
Computational Intelligence Laboratory (CILab), Department of Mathematics, University of Patras, GR-26110 Patras, Greece e-mail: alekst@math.upatras.gr, sotos@math.upatras.gr, vrahatis@math.upatras.gr
Michael N. Vrahatis
Affiliation:
Computational Intelligence Laboratory (CILab), Department of Mathematics, University of Patras, GR-26110 Patras, Greece e-mail: alekst@math.upatras.gr, sotos@math.upatras.gr, vrahatis@math.upatras.gr
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Abstract

A large variety of issues influence the success of data mining on a given problem. Two primary and important issues are the representation and the quality of the dataset. Specifically, if much redundant and unrelated or noisy and unreliable information is presented, then knowledge discovery becomes a very difficult problem. It is well-known that data preparation steps require significant processing time in machine learning tasks. It would be very helpful and quite useful if there were various preprocessing algorithms with the same reliable and effective performance across all datasets, but this is impossible. To this end, we present the most well-known and widely used up-to-date algorithms for each step of data preprocessing in the framework of predictive data mining.

Type
Survey Article
Information
The Knowledge Engineering Review , Volume 34 , 2019 , e1
Copyright
© Cambridge University Press, 2019 

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