Abstract
This research presents a method to select an ideal feature subset of original and transformed features. The feature selection method utilizes a genetic wrapper scheme that employs classification accuracy as its fitness function. The feature subset generated by the proposed approach usually contains features produced by different transformation schemes. The selection of transformed features provides new insight on the interactions and behaviors of the features. This method is especially effective with temporal data and provides knowledge about the dynamic nature of the process. This method was successfully applied to optimize efficiency of a circulating fluidized bed boiler at a local power plant. The computational results from the power plant demonstrate an improvement in classification accuracy, reduction in the number of rules, and decrease in computational time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kusiak, A.: Feature Transformation Methods in Data Mining. IEEE Transactions on Electronic Packaging Manufacturing 24(3), 214–221 (2001)
Weigend, A., Chen, F., Figlewski, S., Waterhouse, S.R.: Discovering Technical Trades in the T-Bond Futures Market. In: Argawal, R., Stolorz, P., Piatetsky-Shapiro, G. (eds.) Proc. Fourth Int’l Conf. Knowledge Discovery and Data Mining (KDD 1998), pp. 354–358 (1998)
Vafaie, H., De Jong, K.: Feature Space Transformation Using Genetic Algorithms. IEEE Intelligent Systems 13(2), 57–65 (1998)
Hubbard, B.B.: The World According to Wavelets: The Story of a Mathematical Technique in the Making. In: Peters, A.K. (ed.), 2nd edn. Natick, Massachusetts (1998)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA (1989)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)
Fayyad, U., Piatetsky-Shapiro, Smyth Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press (1995)
Hu, M.Y., Xie, H., Tiong, T.B., Wu, X.: Study on a Spatially Selective Noise Filtration Technique for Suppressing Noises in Partial Discharge On-line Monitoring. In: Proceedings of the 6th International Conference on Properties and Applications of Dielectric Materials, vol. 2, pp. 689–692 (2000)
Hu, M.Y., Jiang, X., Xie, H., Wang, Z.: A New Technique For Extracting Partial Discharge Signals In On-Line Monitoring with Wavelet Analysis. In: Proceedings of 1998 International Symposium on Electrical Insulating Materials, pp. 677–680 (1998)
Huang, C.M., Huang, Y.C.: Combined Wavelet-Based Networks and Game-Theoretical Decision Approach for Real-Time Power Dispatch. IEEE Transactions on Power Systems 17(3), 633–639 (2002)
Smith, K., Perez, R.: Locating partial discharges in a power generating system during neural networks and wavelets. In: Annual Report Conference on Electrical Insulation and Dielectric Phenomena, pp. 458–461 (2002)
Masugi, M.: Multiresolution Analysis of Electrostatic Discharge Current from Electromagnetic Intereference Aspects. IEEE Transactions on Electromagnetic Compatibility 45(2), 393–403 (2003)
Stone, M.: Cross-validatory choice and assessment of statistical classifications. Journal of the Royal Statistical Society 36, 111–147 (1974)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Burns, A., Kusiak, A., Letsche, T. (2004). Mining Transformed Data Sets. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-30132-5_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
Online ISBN: 978-3-540-30132-5
eBook Packages: Springer Book Archive