| Download | - View accepted manuscript: Cost-sensitive Feature Reduction Applied to a Hybrid Genetic Algorithm (PDF, 622 KiB)
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| Author | Search for: Lavrac, N.; Search for: Gamberger, D.; Search for: Turney, Peter1 |
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| Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
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| Format | Text, Article |
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| Conference | Proceedings of the Seventh International Workshop on Algorithmic Learning Theory (ALT'96), October 1996., Sydney, Australia |
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| Subject | hybrid genetic algorithm |
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| Abstract | This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in pre-processing before starting the learning process. A case study of data pre-processing for a hybrid genetic algorithm shows that the elimination of irrelevant features can substantially improve the efficiency of learning. In addition, cost-sensitive feature elimination can be effective for reducing costs of induced hypotheses. |
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| Publication date | 1996 |
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| Language | English |
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| NRC number | NRCC 40167 |
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| NPARC number | 5751298 |
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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 | 8847c59c-20ce-4578-a925-17528be99ba3 |
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| Record created | 2008-12-02 |
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| Record modified | 2020-03-20 |
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