Consolidated trees versus bagging when explanation is required
In some real-world problems solved by machine learning it is compulsory for the solution provided to be comprehensible so that the correct decision can be made. It is in this context that this paper compares bagging (one of the most widely used multiple classifier systems) with the consolidated tree...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2010 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/71478 |
| Acceso en línea: | http://hdl.handle.net/10810/71478 |
| Access Level: | acceso abierto |
| Palabra clave: | machine learning multiple classifier systems bagging decision trees comprehensibility |
| Sumario: | In some real-world problems solved by machine learning it is compulsory for the solution provided to be comprehensible so that the correct decision can be made. It is in this context that this paper compares bagging (one of the most widely used multiple classifier systems) with the consolidated trees construction (CTC) algorithm, when the learning problem to be solved requires the classification made to be provided with an explanation. Bearing in mind the comprehensibility shortcomings of bagging, the Domingos' proposal, called combining multiple models, has been used to address this problem. The two algorithms have been compared from three main points of view: accuracy, quality of the explanation the classification is provided with, and computational cost. The results obtained show that it is beneficial to use CTC in situations where an explanation is required, because: CTC has a greater discriminating capacity than the explanation extraction algorithm added to bagging; the explanation provided is of a greater quality; it is simpler and more reliable; and CTC is computationally more efficient. |
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