The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Classification by ensembles from random partitions of high-dimensional data
Computational Statistics & Data Analysis
Ensemble methods for classification of patients for personalized medicine with high-dimensional data
Artificial Intelligence in Medicine
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Collective-agreement-based pruning of ensembles
Computational Statistics & Data Analysis
A model-free ensemble method for class prediction with application to biomedical decision making
Artificial Intelligence in Medicine
Artificial Intelligence Review
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
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Subpopulation-specific confidence designation for more informative biomedical classification
Artificial Intelligence in Medicine
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Objective: Classification algorithms can be used to predict risks and responses of patients based on genomic and other high-dimensional data. While there is optimism for using these algorithms to improve the treatment of diseases, they have yet to demonstrate sufficient predictive ability for routine clinical practice. They generally classify all patients according to the same criteria, under an implicit assumption of population homogeneity. The objective here is to allow for population heterogeneity, possibly unrecognized, in order to increase classification accuracy and further the goal of tailoring therapies on an individualized basis. Methods and materials: A new selective-voting algorithm is developed in the context of a classifier ensemble of two-dimensional convex hulls of positive and negative training samples. Individual classifiers in the ensemble are allowed to vote on test samples only if those samples are located within or behind pruned convex hulls of training samples that define the classifiers. Results: Validation of the new algorithm's increased accuracy is carried out using two publicly available datasets having cancer as the outcome variable and expression levels of thousands of genes as predictors. Selective voting leads to statistically significant increases in accuracy from 86.0% to 89.8% (p