NDLI: Ensemble particle swarm model selection

Content Provider	IEEE Xplore Digital Library
Author	Escalante, H.J. Montes, M. Sucar, E.
Copyright Year	2010
Description	Author affiliation: Department of Computer Science, at the National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, 72840, Mexico (Escalante, H.J.; Montes, M.; Sucar, E.)
Abstract	This paper elaborates on the benefits of using particle swarm model selection (PSMS) for building effective ensemble classification models. PSMS searches in a toolbox for the best combination of methods for preprocessing, feature selection and classification for generic binary classification tasks. Throughout the search process PSMS evaluates a wide variety of models, from which a single solution (i.e. the best classification model) is selected. Satisfactory results have been reported with the latter formulation in several domains. However, many models that are potentially useful for classification are disregarded for the final model. In this paper we propose to re-use such candidate models for building effective ensemble classifiers. We explore three simple formulations for building ensembles from intermediate PSMS solutions that do not require of further computation than that of the traditional PSMS implementation. We report experimental results on benchmark data as well as on a data set from object recognition. Our results show that better models can be obtained with the ensemble version of PSMS, motivating further research on the combination of candidate PSMS models. Additionally, we analyze the diversity of the classification models, which is known to be an important factor for the construction of ensembles.
Starting Page	1
Ending Page	8
File Size	598672
Page Count	8
File Format	PDF
ISBN	9781424469161
ISSN	10987576
e-ISBN	9781424469185
DOI	10.1109/IJCNN.2010.5596915
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Buildings Data models Feature extraction Accuracy Particle swarm optimization Predictive models
Content Type	Text
Resource Type	Article

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