| Autor: |
Pacheco, Angel Díaz, Reyes-García, Carlos Alberto |
| Předmět: |
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| Zdroj: |
International Conference Connected Smart Cities; 2018, p19-26, 8p |
| Abstrakt: |
One of the main concerns in the pattern recognition field is enhancing the predictive accuracy of the classification algorithms. While some efforts are focused in the development of new algorithms, another approach is centered in the search of the combination of data preparation techniques, a subset of features and, the selection of the adequate learning algorithm with its hyper parameters tuned. This approach is known as Full Model Selection (FMS) and because of the high search space, this paradigm is largely unexplored especially in large size datasets. In this work, the use of the meta-learning approach is suggested as a way to solve the FMS problem in huge datasets and retain the knowledge gained of each analysis performed which would be lost in another way. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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