Abstract
Feature selection is an important data preprocessing technique in classification problems. This paper focuses on a new feature selection problem, in which sampling data of different features have different reliability degree. First, the problem is modeled as a multi-objective optimization. There two objectives should be optimized simultaneously: reliability and classifying accuracy of feature subset. Then, a multi-objective feature selection method based on particle swarm optimization, called JMOPSO, is proposed by incorporating several effective operators. Finally, experimental results suggest that the proposed JMOPSO is a highly competitive feature selection method for solving the feature selection problem with unreliable data.
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Zhang, Y., Xia, C., Gong, D., Sun, X. (2014). Multi-objective PSO Algorithm for Feature Selection Problems with Unreliable Data. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_44
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DOI: https://doi.org/10.1007/978-3-319-11857-4_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11856-7
Online ISBN: 978-3-319-11857-4
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