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Pruning classification rules with instance reduction methods

Othman, O; Bryant, CH

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Authors

O Othman



Abstract

Generating classification rules from data often leads to large sets of rules that need to be pruned. A new pre-pruning technique for rule induction is presented which applies instance reduction before rule induction. Training three rule classifiers on datasets that have been reduced earlier with instance reduction methods leads to a statistically significant lower number of generated rules, without adversely affecting the predictive performance. The search strategies used by the three algorithms vary in terms of both type (depth-first or beam search) and direction (general-to-specific or specific-to-general).

Citation

Othman, O., & Bryant, C. (2015). Pruning classification rules with instance reduction methods. International journal of machine learning and computing (Online), 5(3), 187-191. https://doi.org/10.7763/IJMLC.2015.V5.505

Journal Article Type Article
Publication Date Jun 8, 2015
Deposit Date Jan 7, 2015
Publicly Available Date Apr 5, 2016
Journal International Journal of Machine Learning and Computing
Peer Reviewed Peer Reviewed
Volume 5
Issue 3
Pages 187-191
DOI https://doi.org/10.7763/IJMLC.2015.V5.505
Keywords Rule Induction,
Noise Filtering,
Instance Reduction.
Publisher URL http://dx.doi.org/10.7763/IJMLC.2015.V5.505
Related Public URLs http://www.salford.ac.uk/computing-science-engineering/cse-academics/chris-bryant

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