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Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
Abstract
Evolutionary algorithms are adaptive methods based on natural evolution that may be used for search and optimization. As data reduction in knowledge discovery in databases (KDDs) can be viewed as a search problem, it could be solved using evolutionary algorithms (EAs). In this paper, we have carried out an empirical study of the performance of four representative EA models in which we have taken into account two different instance selection perspectives, the prototype selection and the training set selection for data reduction in KDD. This paper includes a comparison between these algorithms and other nonevolutionary instance selection algorithms. The results show that the evolutionary instance selection algorithms consistently outperform the nonevolutionary ones, the main advantages being: better instance reduction rates, higher classification accuracy, and models that are easier to interpret.
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Cited by
An efficient instance selection algorithm to reconstruct training set for support vector machine
Liu, C., Wang, W., Wang, M., Lv, F., & Konan, M. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.10.031 · Cited by 146
Wahono (2007) · doi:10.3923/jse.2007.1.12 · Cited by 113
Selecting Representative Data Sets
Borovicka, T., Jirina, M., Kordik, P., & Jiri, M. (2012) · Advances in Data Mining Knowledge Discovery and Applications · doi:10.5772/50787 · Cited by 94
Fast instance selection for speeding up support vector machines
Chen, J., Zhang, C., Xue, X., & Liu, C.-L. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.01.031 · Cited by 53
Metadata
- Authors
- Cano, J. R., Herrera, F., & Lozano, M.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2003-12
- Volume
- 7
- Issue
- 6
- Pages
- 561-575
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2135547590
- Cited by
- 332
- References
- 0
- FWCI
- 6.36
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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