| DOI | Resolve DOI: https://doi.org/10.1109/ICMLA.2009.37 |
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| Author | Search for: Al-Obeidat, F.; Search for: Belacel, N.; Search for: Mahanti, P.; Search for: Carretero, J. A. |
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| Format | Text, Article |
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| Conference | Eighth International Conference On Machine Learning and Applications (IEEE Computer Society), Miami, Florida, December 13–15, 2009 |
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| Subject | Information and Communications Technologies |
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| Abstract | This paper introduces new techniques for learning the classification method PROAFTN from data. PROAFTN is a multi-criteria classification method and belongs to the class of supervised learning algorithms. To use PROAFTN for classification, some parameters must be obtained for this purpose. Therefore, an automatic method to extract these parameters from data with minimum classification errors is required. Here, discretization techniques and genetic algorithms are proposed for establishing these parameters and then building the classification model. Based on the obtained results, the newly proposed approach outperforms widely used classification methods. |
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| Publication date | 2009-12-15 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 15261135 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 1ef60dc2-ec9d-4a91-9ea8-72681838d195 |
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| Record created | 2010-06-10 |
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| Record modified | 2020-04-16 |
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