Abstract
As detailed in Chap. 4, features have been extracted from the pre-processed data. Too many features may lead to the curse of dimensionality issues. This chapter explains how to obtain a set of relevant features which is the process of feature selection. Feature selection is the process in which most informative variables are selected for the generation of the model. It helps to remove the redundant data and contributes to proper classification. While minimizing the redundancy, we should keep in mind that the predicted information must be preserved as much as possible. This chapter describes various feature selection methods such as Principal Component Analysis (PCA)-based approach, Mutual Information (MI), Bhattacharyya Distance (BD), and Independent Component Analysis (ICA). A novel feature selection based on graphical review has also been elaborated later in this chapter.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Verma, N.K., Maini, T., Salour, A.: Acoustic signature based intelligent health monitoring of air compressors with selected features. In: 9th International Conference on Information Technology: New Generations, Nevada, USA, pp. 839–845 (2012)
Shlens, J.: A Tutorial on Principal Component Analysis. University of California, Systems Neurobiology Lab., California, USA (2005)
Thirukovalluru, R., Sevakula, R.K., Dixit, S., Verma, N.K.: Generating optimum feature sets for fault diagnosis using denoising stacked auto-encode. In: IEEE International Conference on Prognostics and Health Management, Canada, USA, pp. 1–7 (2016)
Verma, N.K, Gupta, V.K., Sharma, M., Sevakula, R.K.: Intelligent condition based monitoring of rotating machines using sparse auto-encoders. In: IEEE Conference on Prognostics and Health Management, Maryland, USA, pp. 1–7 (2013)
Maurya, S., Singh, V., Dixit, S., Verma, N.K., Salour, A., Liu, J.: Fusion of low-level features with stacked autoencoder for condition based monitoring of machines. In: IEEE International Conference on Prognostics and Health Management, Washington, USA, pp. 11–13 (2018)
Sharma, A.K., Singh, V., Verma, N.K., Liu, J.: Condition based monitoring of machine using Mamdani fuzzy network. In: Prognostics and System Health Management Conference, Chongqing, China, pp. 26–28 (2018)
Verma, N.K., Dixit, S., Sevakula, R.K., Salour, A.: Computational framework for machine fault diagnosis with autoencoder variants. In: 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, Xi’an, China, pp. 15–17 (2018)
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)
He, Q., Yan, R., Kong, F., Du, R.: Machine condition monitoring using principal component representations. J. Mech. Syst. Signal Pr. 23(2), 446–466 (2009)
Battiti, R.: Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Netw. 5(4), 537–550 (1994)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max dependency, max-relevance and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)
Kwak, N., Choi, C.H.: Input feature selection for classification problems. IEEE Trans. Neural Netw. 3(1), 143–159 (2002)
Estevez, P.A., Tesmer, M., Perez, C.A., Zurada, J.M.: Normalized mutual information feature selection. IEEE Trans. Neural Netw. 20(2), 189–201 (2009)
Guorong, X., Peiqi, C., Minhui, W.: Bhattacharyya distance feature selection. In: Proceedings 13th IEEE International Conference on Pattern Recognition, 2, pp. 195–199 (1996)
Aapo, H., Karhunen, J., Oja, E.: Independent Component Analysis, vol. 46. Wiley (2004)
Verma, N.K., Sevakula, R.K., Thirukovalluru, R.: Pattern analysis framework with graphical indices for condition based monitoring. IEEE Trans. Rel. 66(4), 1085–1100 (2017)
Verma, N.K., Sevakula, R.K., Dixit, S., Salour, A.: Intelligent condition based monitoring using acoustic signals for air compressors. IEEE Trans. Rel. 65(1), 291–309 (2016)
Verma, N.K., Singh, S., Gupta, J.K., Sevakula, R.K., Dixit, S., Salour, A.: Smartphone application for fault recognition. In: 6th International Conference on Sensing Technology, Kolkata, India, pp. 1–6 (2012)
Verma, N.K., Singh, J.V., Gupta, M., Sevakula, R.K., Dixit, S.: Windows mobile and tablet app for acoustic signature machine health monitoring. In: International Conference on Industrial and Information Systems, Gwalior, India, pp. 1–6 (2014)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Verma, N.K., Salour, A. (2020). Feature Selection. In: Intelligent Condition Based Monitoring. Studies in Systems, Decision and Control, vol 256. Springer, Singapore. https://doi.org/10.1007/978-981-15-0512-6_5
Download citation
DOI: https://doi.org/10.1007/978-981-15-0512-6_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0511-9
Online ISBN: 978-981-15-0512-6
eBook Packages: EngineeringEngineering (R0)