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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Olmo, J.L. Romero, J.R. Ventura, S. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Computer Science and Numerical Analysis, University of Cordoba, Rabanales Campus, Albert Einstein building, 14071 Cordoba, Spain (Olmo, J.L.; Romero, J.R.; Ventura, S.) |
| Abstract | This paper focuses on the application of a new ACO-based automatic programming algorithm to the classification task of data mining. This new model, called GBAP algorithm, is based on a context-free grammar that properly guides the creation of new valid individuals. Moreover, its most differentiating factors, such as the use of two complementary heuristic measures for every transition rule, as well as the way it assigns a consequent and evaluates the extracted rules, are also discussed. These features enhance the final rule compilation from the output classifier. The performance of the proposed algorithm is evaluated and compared against other top algorithms, and the results obtained over 17 diverse data sets show that our approach reaches pretty competitive and even better accuracy values than those resulting from the other algorithms considered in the experimentation. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 702953 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469093 |
| e-ISBN | 9781424469116 |
| DOI | 10.1109/CEC.2010.5586492 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-07-18 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Grammar Production Training Data mining Prediction algorithms Automatic programming |
| Content Type | Text |
| Resource Type | Article |
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