Abstract
An evolutionary singular value decomposition (SVD) entropy based feature selection approach is proposed for finding optimal features among large data sets. Since the data typically consists of a large number of features, all of them are not optimal. In this paper, an optimal feature selection approach based on differential evolution (DE) and SVD entropy is proposed. The functioning of the proposed approach is examined on available UCI data sets. This approach provides ranked features by optimizing SVD entropy using the DE. An SVD entropy based fitness function is employed as the criterion to measure the optimal features and this makes the new approach easier to implement. DE results in a faster and accurate convergence towards global optima. The proposed approach shows its effectiveness on binary data sets with a number of features ranging between 9 and 60. The result explains that the proposed approach can converge quickly and rank the features. The experimental section demonstrates the results in terms of classification accuracy by Support Vector Machine (SVM) and Naive Bayes (NB) classifiers. The explored results are favorable and strengthen the contribution of the proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier (2011)
Xing, E.P., Jordan, M.I., Karp, R.M., et al.: Feature selection for high-dimensional genomic microarray data. ICML 1, 601–608 (2001)
Jain, A., Zongker, D.: Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 153–158 (1997)
Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice Hall (1982)
Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)
Alter, O., Brown, P.O., Botstein, D.: Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. 97(18), 10101–10106 (2000)
Chao, Y., Dai, M., Chen, K., Chen, P., Zhang, Z.: Fuzzy entropy based multilevel image thresholding using modified gravitational search algorithm. In: 2016 IEEE International Conference on Industrial Technology (ICIT), pp. 752–757. IEEE (2016)
Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer Science & Business Media (2006)
Golub, G.H., Van Loan, C.F.: Matrix Computations, vol. 3. JHU Press (2012)
Rashmi, Ghose, U., Anika: A data reduction method based on indiscernibility and rough entropy for uncertain system. In: International Conference on Intelligent Systems (IntelliSys), pp. 482–487. IEEE (2017)
Rashmi, Ghose, U., Mehta, R.: Attribute reduction using the combination of entropy and fuzzy entropy. In: Networking Communication and Data Knowledge Engineering, pp. 169–177. Springer (2018)
Shannon, C.E., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press (2015)
Banerjee, M., Pal, N.R., Some modification and extension: Feature selection with SVD entropy. Inf. Sci. 264, 118–134 (2014)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer Science & Business Media (2013)
Mitchell, T.M.: Machine Learning. Engineering/Math, vol. 1. McGraw-Hill Science (1997)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: \(\{\text{UCI}\}\) Repository of Machine Learning Databases (1998)
Demouy, J., Chamberlain, J., Harris, M., Marchand, L.H.: The Pima Indians: pathfinders of health. National Institute of Diabetes Digestive Kidney Diseases, Bethesda, MD Google Scholar (1995)
Kodratoff, Y.: Introduction to Machine Learning. Morgan Kaufmann (2014)
Rish, I.: An empirical study of the Naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, pp. 41–46. IBM (2001)
Williamson, D.F., Parker, R.A., Kendrick, J.S.: The box plot: a simple visual method to interpret data. Ann. Internal Med. 110(11), 916–921 (1989)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rashmi, Ghose, U. (2019). A Novel Differential Selection Method Based on Singular Value Decomposition Entropy for Solving Real-World Problems. In: Lee, R. (eds) Computer and Information Science. ICIS 2018. Studies in Computational Intelligence, vol 791. Springer, Cham. https://doi.org/10.1007/978-3-319-98693-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-98693-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98692-0
Online ISBN: 978-3-319-98693-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)