Título
Particle Swarm Model Selection
Autor
HUGO JAIR ESCALANTE BALDERAS
MANUEL MONTES Y GOMEZ
LUIS ENRIQUE SUCAR SUCCAR
Nivel de Acceso
Acceso Abierto
Materias
Full model selection - (FULL MODEL SELECTION) Machine learning challenge - (MACHINE LEARNING CHALLENGE) Particle swarm optimization - (PARTICLE SWARM OPTIMIZATION) Experimentation - (EXPERIMENTATION) Cross validation - (CROSS VALIDATION) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)
Resumen o descripción
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.
Editor
Journal of Machine Learning Research
Fecha de publicación
2009
Tipo de publicación
Artículo
Versión de la publicación
Versión aceptada
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Estudiantes
Investigadores
Público en general
Sugerencia de citación
Escalante-Balderas, H.J., et al., (2009). Particle Swarm Model Selection, Journal of Machine Learning Research (10): 405-440
Repositorio Orígen
Repositorio Institucional del INAOE
Descargas
312