- Title
- Evolutionary data mining approaches for rule-based and tree-based classifiers
- Creator
- Weise, Thomas; Chiong, Raymond
- Relation
- 9th IEEE International Conference on Cognitive Informatics (ICC1 2010). Proceedings of the 9th IEEE International Conference on Cognitive Informatics (Beijing 7-9 July, 2010) p. 696-703
- Publisher Link
- http://dx.doi.org/10.1109/COGINF.2010.5599821
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2010
- Description
- Data mining is an important process, with applications found in many business, science and industrial problems. While a wide variety of algorithms have already been proposed in the literature for classification tasks in large data sets, and the majority of them have been proven to be very effective, not all of them are flexible and easily extensible. In this paper, we introduce two new approaches for synthesizing classifiers with Evolutionary Algorithms (EAs) in supervised data mining scenarios. The first method is based on encoding rule sets with bit string genomes and the second one utilizes Genetic Programming to create decision trees with arbitrary expressions attached to the nodes. Comparisons with some sophisticated standard approaches, such as C4.5 and Random-Forest, show that the performance of the evolved classifiers can be very competitive. We further demonstrate that both proposed approaches work well across different configurations of the EAs.
- Subject
- data mining; evolutionary algorithms; rule-based classifiers; decision trees
- Identifier
- http://hdl.handle.net/1959.13/1057539
- Identifier
- uon:16204
- Identifier
- ISBN:9781424480401
- Language
- eng
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