Abstract
Population-based (or evolutionary) algorithms have been attracting an increased attention due to their powerful search capabilities. For the particular problem of feature selection, population-based methods aim to produce better ”or fitter” future generations that contain more informative subsets of features. It is well-known that feature subset selection is a very challenging optimization problem, especially when dealing with datasets that contain large number of features. Most of the commonly used population-based feature selection methods use operators that do not take into account relationships between features to generate future subsets, which can have an impact on their capabilities to properly explore the search space. We present here a new population-based feature selection method that utilize dependency between features to guide the search. In addition, a novel method for estimating dependency between feature pairs is proposed based on the concept of fuzzy entropy. Results obtained from datasets with various sizes indicate the superiority of the proposed method in comparison to some of the well-known methods in the literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A.: Feature Extraction: Foundations and Applications. Springer, New York (2006)
Lin, S.-W., Tseng, T.-Y., Chou, S.-Y., Chen, S.-C.: A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks. Expert Systems with Applications 34(2), 1491–1499 (2008)
Frohlich, H., Chapelle, O., Scholkopf, B.: Feature Selection for Support Vector Machines by Means of Genetic Algorithms. In: Proc. IEEE Intl. Conf. Tools with Artificial Intelligence (ICTAI 2003), pp. 142–148 (2003)
Al-Ani, A.: Feature Subset Selection Using Ant Colony Optimization. Int. Journal of Computational Intelligence 2, 53–58 (2005)
Firpi, H.A., Goodman, E.: Swarmed Feature Selection. In: Proc. Applied Imagery Pattern Recognition Workshop, pp. 112–118 (2004)
Oh, I.S., Lee, J.S., Moon, B.R.: Hybrid Genetic Algorithms for Feature Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11), 1424–1437 (2004)
Al-Ani, A.: A dependency-based search strategy for feature selection. Expert Systems with Applications 36(10), 12392–12398 (2009)
De Luca, A., Termini, S.: A definition of a nonprobabilistic entropy in the setting of fuzzy set theory. Information and Control 20, 301–312 (1972)
Khushaba, R.N., Kodagoda, S., Lal, S., Dissanayake, G.: Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm 58(1), 121–131 (2011)
AlSukker, A., Khushaba, R., Al-Ani, A.: Enhancing the Diversity of Genetic Algorithm for Improved Feature Selection. In: IEEE International Conference on Systems, Man, and Cybernetics, SMC 2010 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Al-Ani, A., Khushaba, R.N. (2012). A Population Based Feature Subset Selection Algorithm Guided by Fuzzy Feature Dependency. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-35326-0_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35325-3
Online ISBN: 978-3-642-35326-0
eBook Packages: Computer ScienceComputer Science (R0)