The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Programming
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
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
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
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
Journal of Biomedical Informatics
Selective voting in convex-hull ensembles improves classification accuracy
Artificial Intelligence in Medicine
International Journal of Automation and Computing
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Objective: A classification algorithm that utilizes two-dimensional convex hulls of training-set samples is presented. Methods and material: For each pair of predictor variables, separate convex hulls of positive and negative samples in the training set are formed, and these convex hulls are used to classify test points according to a nearest-neighbor criterion. An ensemble of these two-dimensional convex-hull classifiers is formed by trimming the "mC"2 possible classifiers derived from the m predictors to a set of classifiers comprised of only unique predictor variables. Because only two-dimensional spaces are required to be populated by training-set samples, the ''curse of dimensionality'' is not an issue. At the same time, the power of ensemble voting is exploited by combining the classifications of the unique two-dimensional classifiers to reach a final classification. Results: The algorithm is illustrated by application to three publicly available biomedical data sets with genomic predictors and is shown to have prediction accuracy that is competitive with a number of published classification procedures. Conclusion: Because of its superior performance in terms of sensitivity and negative predictive value compared to its competitors, the convex-hull ensemble classifier demonstrates good potential for medical screening, where often the major emphasis is placed on having reliable negative predictions.