Abstract
This paper proposes hybrid feature selection algorithms to build the efficient diagnostic models based on a new accuracy criterion, generalized F-score (GF) and SVM. The hybrid algorithms adopt Sequential Forward Search (SFS), and Sequential Forward Floating Search (SFFS), and Sequential Backward Floating Search (SBFS), respectively, with SVM to accomplish hybrid feature selection with the new accuracy criterion to guide the procedure. We call them as modified GFSFS, GFSFFS and GFSBFS, respectively. These hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the efficient classifiers. To get the best and statistically meaningful classifiers, we not only conduct 10-fold cross validation experiments on training subset, but also on the whole erythemato-squamous diseases datasets. Experimental results show that our proposed hybrid methods construct efficient diagnosis classifiers with high average accuracy when compared with traditional algorithms.
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
Güvenir, H.A., Demiröz, G., Ílter, N.: Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artificial Intelligence in Medicine 13, 147–165 (1998)
Güvenir, H.A., Emeksiz, N.: An expert system for the differential diagnosis of erythemato-squamous diseases. Expert Systems with Applications 18, 43–49 (2000)
Übeyli, E.D., Güler, Í.: Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems. Computers in Biology and Medicine 35, 421–433 (2005)
Luukka, P., Leppälampi, T.: Similarity classifier with generalized mean applied to medical data. Computers in Biology and Medicine 36, 1026–1040 (2006)
Polat, K., Günes, S.: The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases. Digital Signal Processing 16, 922–930 (2006)
Nanni, L.: An ensemble of classifiers for the diagnosis of erythemato-squamous diseases. Neurocomputing 69, 842–845 (2006)
Luukka, P.: Similarity classifier using similarity measure derived from Yu’s norms in classification of medical data sets. Computers in Biology and Medicine 37, 1133–1140 (2007)
Übeyli, E.D.: Multiclass support vector machines for diagnosis of erythemato-squamous diseases. Expert Systems with Applications 35, 1733–1740 (2008)
Polat, K., Günes, S.: A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Systems with Applications 36, 1587–1592 (2009)
Übeyli, E.D.: Combined neural networks for diagnosis of erythemato-squamous diseases. Expert Systems with Applications 36, 5107–5112 (2009)
Übeyli, E.D., Doǧdu, E.: Automatic detection of erythemato-squamous diseases using k-Means clustering. Journal of Medical System 34, 179–184 (2010)
Liu, H.W., Sun, J.G., et al.: Feature selection with dynamic mutual information. Pattern Recognition 42, 1330–1339 (2009)
Karabatak, M., Ince, M.C.: A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases. Expert Systems with Applications 36, 12500–12505 (2009)
Xie, J.Y., Xie, W.X., Wang, C.X., et al.: A novel hybrid feature selection method based on IFSFFS and SVM for the diagnosis of erythemato-squamous diseases. In: JMLR Workshop and Conference Proceedings. Workshop on Applications of Pattern Analysis, vol. 11, pp. 142–151. MIT Press, Windsor (2010)
Xie, J.Y., Wang, C.X.: Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases. Expert Systems With Applications 38, 5809–5815 (2011)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Fu, K.S., Min, P.J., Li, T.J.: Feature selection in pattern recognition. IEEE Transactions on Systems Science and Cybernetics SSC 6, 33–39 (1970)
Chen, Y.W., Lin, C.J.: Combining SVMs with various feature selection strategies (2005), http://www.csie.ntu.edu.tw/~cjlin/papers/features.pdf
Kohavi, R., John, G.: Wrappers for feature selection. Artificial Intelligence 1(2), 273–324 (1997)
Blum, A., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 1(2), 245–271 (1997)
Whitney, A.W.: A direct method of nonparametric measurement selection. IEEE Transactions on Computers 20, 1100–1103 (1971)
Marill, T., Green, D.M.: On the effectiveness of receptors in recognition systems. IEEE Transactions on Information Theory IT 9, 11–17 (1963)
Pudil, P., Novovicova, J., Kittler, J.: Floating search method in feature selection. Pattern Recognition Letters 15, 119–1254 (1994)
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
Xie, J., Lei, J., Xie, W., Gao, X., Shi, Y., Liu, X. (2012). Novel Hybrid Feature Selection Algorithms for Diagnosing Erythemato-Squamous Diseases. In: He, J., Liu, X., Krupinski, E.A., Xu, G. (eds) Health Information Science. HIS 2012. Lecture Notes in Computer Science, vol 7231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29361-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-29361-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29360-3
Online ISBN: 978-3-642-29361-0
eBook Packages: Computer ScienceComputer Science (R0)