| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560787 |
| ISSN: | 1405-5546 |
| Autores: | Cepero Pérez, Nayma1 Moreno Espino, Mailyn1 García Borroto, Milton1 Morales, Eduardo F.2 |
| Instituciones: | 1Universidad Tecnológica de La Habana “José Antonio Echeverría”, Facultad de Informática, Cuba 2Instituto Nacional de Astrofísica Óptica y Electrónica, Puebla. México |
| Año: | 2023 |
| Periodo: | Ene-Mar |
| Volumen: | 27 |
| Número: | 1 |
| Paginación: | 89-97 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | Decision forests improve their predictive power based on the combination of various decision trees. The number of trees to be used to achieve the best possible accuracy is not preset and has to be determined by a trial and error process. In many classification problems more trees are used than necessary. This paper introduces a new method, called Progressive Forest, that progressively evaluates the addition of new decision trees into a decision forest to decide when adding more trees is not longer useful. This method was incorporated into the construction schemes of Proactive Forest and Random Forest with very encouraging results. It is experimentally shown that Progressive Forest reduces the number of trees while maintaining the accuracy of the classification. Progressive Forest can be incorporated into any scheme of construction of ensemble, which presents similar characteristics to Random Forest. |
| Keyword: | Ensemble size, Accuracy, Decision forest |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
