The DOI matches its Crossref record.
ROUGH SET REDUCTION OF ATTRIBUTES AND THEIR DOMAINS FOR NEURAL NETWORKS
Abstract
This paper presents an empirical study of the use of the rough set approach to reduction of data for a neural network classifying objects described by quantitative and qualitative attributes. Two kinds of reduction are considered: reduction of the set of attributes and reduction of the domains of attributes. Computational tests were performed with five data sets having different character, for original and two reduced representations of data. The learning time acceleration due to data reduction is up to 4.72 times. The resulting increase of misclassification error does not exceed 11.06%. These promising results let us claim that the rough set approach is a useful tool for preprocessing of data for neural networks.
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Citations
Cited by
Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches
Jensen, R., & Shen, Q. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.96 · Cited by 667
Metadata
- Authors
- Jelonek, J., Krawiec, K., & Slowiński, R.
- Venue
- Computational Intelligence
- Publisher
- Wiley
- Published
- 1995-05
- Volume
- 11
- Issue
- 2
- Pages
- 339-347
- ISSN
- 0824-7935 (ISSN-L)
- 1467-8640
- OpenAlex
- W2168287352
- Cited by
- 191
- References
- 0
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: ROUGH SET REDUCTION OF ATTRIBUTES AND THEIR DOMAINS FOR NEURAL NETWORKS. https://app.cochat.ai/papers/10.1111/j.1467-8640.1995.tb00036.x
- The paper
- Jelonek, J., Krawiec, K., & Slowiński, R. (1995). ROUGH SET REDUCTION OF ATTRIBUTES AND THEIR DOMAINS FOR NEURAL NETWORKS. Computational Intelligence. https://doi.org/10.1111/j.1467-8640.1995.tb00036.x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
