The nature of statistical learning theory
The nature of statistical learning theory
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Machine Learning
Theoretical and Experimental Analysis of a Two-Stage System for Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
MultiStage Cascading of Multiple Classifiers: One Man's Noise is Another Man's Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machines with Embedded Reject Option
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Growing a multi-class classifier with a reject option
Pattern Recognition Letters
WND-CHARM: Multi-purpose image classification using compound image transforms
Pattern Recognition Letters
Object- and spatial-level quantitative analysis of multispectral histopathology images for detection and characterization of cancer
Classification with reject option in gene expression data
Bioinformatics
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Machine learning in medical imaging
Machine Vision and Applications
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Accurate and reliable classification of microscopic biopsy images is an important issue in computer assisted breast cancer diagnosis. In this paper, a new cascade Random Subspace ensembles scheme with reject options is proposed for microscopic biopsy image classification. The classification system is built as a serial fusion of two different Random Subspace classifier ensembles with rejection options to enhance the classification reliability. The first ensemble consists of a set of Support Vector Machine classifiers that converts the original $$K$$-class classification problem into a number of $$K$$ 2-class problems. The second ensemble consists of a Multi-Layer Perceptron ensemble, that focuses on the rejected samples from the first ensemble. For both of the ensembles, the reject option is implemented by relating the consensus degree from majority voting to a confidence measure, and abstaining to classify ambiguous samples if the consensus degree is lower than some threshold. We also investigated the effectiveness of a feature description approach by combining Local Binary Pattern (LBP) texture analysis, statistics derived using the Gray Level Co-occurrence Matrix (GLCM) and the Curvelet Transform. While the LBP analysis efficiently describes local texture properties and the GLCM reflects global texture statistics, the Curvelet Transform is particularly appropriate for the representation of piece-wise smooth images with rich edge information. The combined feature description thus provides a comprehensive biopsy image characterization by taking advantages of their complementary strengths. Using a benchmark microscopic biopsy image dataset, obtained from the Israel Institute of Technology, a high classification accuracy of $$99.25 \%$$ was obtained (with a rejection rate of $$1.94 \%$$) using the proposed system.