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  Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions

Ramirez-Villegas, J., & Ramirez-Moreno, D. (2012). Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions. Neurocomputing, 77(1), 82-100. doi:10.1016/j.neucom.2011.08.015.

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Ramirez-Villegas, JF1, Author              
Ramirez-Moreno, DF, Author
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1Departament of Physics, Universidad Autónoma de Occidente, Cali, Colombia, ou_persistent22              

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 Abstract: This work develops a support vector and neural-based classification of mammographic regions by applying statistical, wavelet packet energy and Tsallis entropy parameterization. From the first four wavelet packet decomposition levels, four different feature sets were evaluated using two-sample Kolmogorov–Smirnov test (KS-test) and, in one case, principal component analysis (PCA). Feature selection was performed applying a hybrid scheme integrating non-parametric KS-test, correlation analysis, a logistic regression (LR) model and sequential forward selection (SFS). The top selected features (depending on the selected wavelet decomposition level) produced the best classification performances in comparison to other well-known feature selection methods. The classification of the data was carried out using several support vector machine (SVM) schemes and multi-layer perceptron (MLP) neural networks. The new set of features improved significantly the classification performance of mammographic regions using conventional SVMs and MLPs.

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 Dates: 2012-02
 Publication Status: Published in print
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 Identifiers: DOI: 10.1016/j.neucom.2011.08.015
BibTex Citekey: RamirezVillegasR2012
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Title: Neurocomputing
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 77 (1) Sequence Number: - Start / End Page: 82 - 100 Identifier: ISSN: 0925-2312
CoNE: https://pure.mpg.de/cone/journals/resource/954925566733