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Unsupervised discretization method based on adjustable intervals

Bennasar, Mohamed, Setchi, Rossitza ORCID: and Hicks, Yulia Alexandrovna ORCID: 2012. Unsupervised discretization method based on adjustable intervals. Presented at: KES 2012 - 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, San Sebastian, Spain, 10-12 September 2012. Published in: Graña, M., Toro, C., Posada, J., Howlett, R. J. and Jain, L. C. eds. Advances in knowledge-based and intelligent information and engineering systems. Frontiers in Artificial Intelligence and Applications (243) Amsterdam: IOS Press, pp. 79-87. 10.3233/978-1-61499-105-2-79

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Discretization is a process applied to transform continuous data into data with discrete attributes. It makes the learning step of many classification algorithms more accurate and faster. Although many efficient supervised discretization methods have been proposed, unsupervised methods such as Equal Width Discretization (EWD) and Equal Frequency Discretization (EFD) are still in use especially with datasets when classification is not available. Each of these algorithms has its drawbacks. To improve the classification accuracy of EWD, a new method based on adjustable intervals is proposed in this paper. The new method is tested using benchmarking datasets from the UCI repository of machine learning databases; the C4.5 classification algorithm is then used to test the classification accuracy. The experimental results show that the method improves the classification accuracy by about 5% compared to the conventional EWD and EFD methods, and is as good as the supervised Entropy Minimization Discretization (EMD) method.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: IOS Press
ISBN: 978164991045
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Last Modified: 06 Jul 2023 10:19

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