A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
Graphical Models and Image Processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Image thresholding using Tsallis entropy
Pattern Recognition Letters
Logistic regression for data mining and high-dimensional classification
Logistic regression for data mining and high-dimensional classification
Generalization Bounds for the Area Under the ROC Curve
The Journal of Machine Learning Research
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
Diagnosis of breast cancer using Bayesian networks: A case study
Computers in Biology and Medicine
Artificial Intelligence in Medicine
Computers in Biology and Medicine
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
SIBGRAPI '09 Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing
A computer-aided detection system for clustered microcalcifications
Artificial Intelligence in Medicine
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Expert Systems with Applications: An International Journal
A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture
Engineering Applications of Artificial Intelligence
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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.