Incremental gene expression programming classifier with metagenes and data reduction

Joanna Jędrzejowicz , Piotr Jędrzejowicz

Abstract

The paper proposes an incremental Gene Expression Programming classifier. Its main features include using two-level ensemble consisting of base classifiers in form of genes and the upper-level classifier in the form of metagene. The approach enables us to deal with big datasets through controlling computation time using data reduction mechanisms. The user can control the number of attributes used to induce base classifiers as well as the number of base classifiers used to induce metagenes. To optimize the parameter setting phase, an approach based on the Orthogonal Experiment Design principles is proposed, allowing for statistical evaluation of the influence of different factors on the classifier performance. In addition, the algorithm is equipped with a simple mechanism for drift detection. A detailed description of the algorithm is followed by the extensive computational experiment. Its results validate the approach. Computational experiment results show that the proposed approach compares favourably with several state-of-the-art incremental classifiers.
Author Joanna Jędrzejowicz (FMPI / II)
Joanna Jędrzejowicz,,
- Institute of Informatics
, Piotr Jędrzejowicz
Piotr Jędrzejowicz,,
-
Journal seriesComplexity, ISSN 1076-2787, (A 35 pkt)
Issue year2018
Vol2018
Pages1-12
Publication size in sheets0.55
DOIDOI:10.1155/2018/6794067
URL https://doi.org/10.1155/2018/6794067
Languageen angielski
LicenseJournal (articles only); published final; Uznanie Autorstwa (CC-BY); with publication
Score (nominal)35
ScoreMinisterial score = 35.0, 09-12-2018, ArticleFromJournal
Ministerial score (2013-2016) = 35.0, 09-12-2018, ArticleFromJournal
Publication indicators WoS Impact Factor: 2016 = 4.621 (2) - 2016=3.515 (5)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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