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2018 | OriginalPaper | Chapter

Effective Streaming Evolutionary Feature Selection Using Dynamic Optimization

Authors : Abdennour Boulesnane, Souham Meshoul

Published in: Computational Intelligence and Its Applications

Publisher: Springer International Publishing

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Abstract

Feature selection is a key issue in machine learning and data mining. A great deal of effort has been devoted to static feature selection. However, with the assumption that features occur over time, methods developed so far are difficult to use if not applicable. Therefore, there is a need to design new methods to deal with streaming feature selection (SFS). In this paper, we propose the use of dynamic optimization to handle the dynamic nature of SFS with the ultimate goal to improve the quality of the evolving subset of selected features. A hybrid model is developed to fish out relevant features set as unnecessary by an online feature selection process. Experimental results show the effectiveness of the proposed framework compared to some state of the art methods.

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previous chapter FA-SETPOWER-MRTA: A Solution for Solving the Multi-Robot Task Allocation Problem

next chapter Evolutionary Multi-objective Optimization of Business Process Designs with MA-NSGAII

http://featureselection.asu.edu/datasets.php.

http://datam.i2r.a-star.edu.sg/datasets/krbd/.

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Title: Effective Streaming Evolutionary Feature Selection Using Dynamic Optimization
Authors: Abdennour Boulesnane
Souham Meshoul
Publisher: Springer International Publishing
Book: Computational Intelligence and Its Applications
Print ISBN: 978-3-319-89742-4

Electronic ISBN: 978-3-319-89743-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-89743-1_29

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