Two step feature selection: Approximate functional dependency approach using membership values

Date

2004-07-29

Author

Uncu, O
Turksen, IB

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Feature selection is one of the most important issues in fields such as system modelling and pattern recognition. In this study, a new feature selection algorithm that combines feature wrapper and feature filter approaches is proposed in order to identify the significant input variables in systems with continuous domains. The proposed method utilizes functional dependency concept and K-Nearest Neighbourhood method to implement the feature filter and feature wrapper, respectively. It is common to have outliers and noise in real-life data. In order to make the proposed feature selection algorithm noise and outlier resistant, approximate functional dependencies are used by utilizing membership values that inherently cope with uncertainty in the data.

Subject Keywords

Input variables, Filters, Feature extraction, Principal component analysis, Independent component analysis, Industrial engineering, Pattern recognition, Uncertainty, Educational institutions, Noise shaping

URI

https://hdl.handle.net/11511/65789

DOI

https://doi.org/10.1109/fuzzy.2004.1375427

Collections

Department of Industrial Engineering, Conference / Seminar

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Citation Formats

O. Uncu and I. Turksen, “Two step feature selection: Approximate functional dependency approach using membership values,” 2004, p. 1643, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65789.