Abstract
This study analyses different methods of diagnostic feature selection in the problem of classification of the blood cells in leukemia. The analyzed methods belong to the wrapper and filter methods and cover wide range of approaches to feature selection problem. In particular they cover 7 methods, each of them working on different principle. As a results of this preprocessing stage we define the best (according to the applied method) set of features which is next used as the input for the Gaussian kernel SVM classifier. The last step of blood cell recognition is the integration of the results of application of all methods. The numerical results of experiments will be presented and analyzed.
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.
References
Cichocki, A., Amari, S.I.: Adaptive blind signal and image processing. Wiley, New York (2003)
Duda, R.O., Hart, P.E., Stork, P.: Pattern classification and scene analysis. Wiley, New York (2003)
Freund, Y.: Boosting a Weak Learning Algorithm by Majority. Information and Computation 121(2), 256–285 (1995)
Genc, H., Cataltepe, Z., Pearson, T.: A New PCA / ICA Based Feature Selection Method. In: IEEE 15th In Signal Processing and Communications Applications, pp. 1–4 (2007)
Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. J. Mach. Learn. Res. 3(3), 1157–1182 (2003)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using Support Vector Machines. Machine Learning 46, 389–422 (2002)
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Amsterdam (1989)
Kuncheva, L.: Combining pattern classifiers: methods and algorithms. Wiley, New York (2004)
Matlab user manual MathWorks, Natick, USA (2009)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1996)
Osowski, S., Markiewicz, T.: Support vector machine for recognition of white blood cells in leukemia. In: Camps-Valls, G., Rojo-Alvarez, J.L., Martinez-Ramon, M. (eds.) Kernel Methods in Bioengineering, Signal and Image Processing, pp. 93–123. Idea Group Publishing, London (2007)
Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Schurmann, J.: Pattern classification, a unified view of statistical and neural approaches. Wiley, New York (1996)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to data mining. Pearson Education, Inc., Boston (2006)
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
Staroszczyk, T., Osowski, S., Markiewicz, T. (2012). Comparative Analysis of Feature Selection Methods for Blood Cell Recognition in Leukemia. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-31537-4_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31536-7
Online ISBN: 978-3-642-31537-4
eBook Packages: Computer ScienceComputer Science (R0)