The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical and Experimental Analysis of a Two-Stage System for Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Growing a multi-class classifier with a reject option
Pattern Recognition Letters
Classification with reject option in gene expression data
Bioinformatics
Bioinformatics
Fusion of systems for automated cell phenotype image classification
Expert Systems with Applications: An International Journal
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
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Subcellular localisation is a key functional characteristic of proteins. In this paper, we apply Haralick texture analysis and Curvelet Transform for feature description and propose a cascade Random Subspace RS ensemble with rejection options for subcellular phenotype classification. Serial fusions of RS classifier ensembles much improve classification reliability. The rejection 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 a threshold. Using the public 2D HeLa cell images, classification accuracy 93% is obtained with rejection rate 2.7% from the proposed system.