Please use this identifier to cite or link to this item: http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1185
Particle Swarm Model Selection
HUGO JAIR ESCALANTE BALDERAS
MANUEL MONTES Y GOMEZ
LUIS ENRIQUE SUCAR SUCCAR
Acceso Abierto
Atribución-NoComercial-SinDerivadas
Full model selection
Machine learning challenge
Particle swarm optimization
Experimentation
Cross validation
This paper proposes the application of particle swarm optimization (PSO) to the problem of full model selection, FMS, for classification tasks. FMS is defined as follows: given a pool of preprocessing methods, feature selection and learning algorithms, to select the combination of these that obtains the lowest classification error for a given data set; the task also includes the selection of hyperparameters for the considered methods. This problem generates a vast search space to be explored, well suited for stochastic optimization techniques. FMS can be applied to any classification domain as it does not require domain knowledge. Different model types and a variety of algorithms can be considered under this formulation. Furthermore, competitive yet simple models can be obtained with FMS. We adopt PSO for the search because of its proven performance in different problems and because of its simplicity, since neither expensive computations nor complicated operations are needed. Interestingly, the way the search is guided allows PSO to avoid overfitting to some extend. Experimental results on benchmark data sets give evidence that the proposed approach is very effective, despite its simplicity. Furthermore, results obtained in the framework of a model selection challenge show the competitiveness of the models selected with PSO, compared to models selected with other techniques that focus on a single algorithm and that use domain knowledge.
Journal of Machine Learning Research
2009
Artículo
Inglés
Estudiantes
Investigadores
Público en general
Escalante-Balderas, H.J., et al., (2009). Particle Swarm Model Selection, Journal of Machine Learning Research (10): 405-440
CIENCIA DE LOS ORDENADORES
Versión aceptada
acceptedVersion - Versión aceptada
Appears in Collections:Artículos de Ciencias Computacionales

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