The Strength of Weak Learnability
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
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
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine Learning
Machine Learning
Classification by ensembles from random partitions of high-dimensional data
Computational Statistics & Data Analysis
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
Artificial Intelligence in Medicine
A model-free ensemble method for class prediction with application to biomedical decision making
Artificial Intelligence in Medicine
Ensemble gene selection by grouping for microarray data classification
Journal of Biomedical Informatics
Computers in Biology and Medicine
Ensemble gene selection for cancer classification
Pattern Recognition
Wavelet selection for disease classification by DNA microarray data
Expert Systems with Applications: An International Journal
Proceedings of the 1st ACM International Health Informatics Symposium
Selective voting in convex-hull ensembles improves classification accuracy
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis
Expert Systems with Applications: An International Journal
Diverse accurate feature selection for microarray cancer diagnosis
Intelligent Data Analysis
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Objective: Personalized medicine is defined by the use of genomic signatures of patients in a target population for assignment of more effective therapies as well as better diagnosis and earlier interventions that might prevent or delay disease. An objective is to find a novel classification algorithm that can be used for prediction of response to therapy in order to help individualize clinical assignment of treatment. Methods and materials: Classification algorithms are required to be highly accurate for optimal treatment on each patient. Typically, there are numerous genomic and clinical variables over a relatively small number of patients, which presents challenges for most traditional classification algorithms to avoid over-fitting the data. We developed a robust classification algorithm for high-dimensional data based on ensembles of classifiers built from the optimal number of random partitions of the feature space. The software is available on request from the authors. Results: The proposed algorithm is applied to genomic data sets on lymphoma patients and lung cancer patients to distinguish disease subtypes for optimal treatment and to genomic data on breast cancer patients to identify patients most likely to benefit from adjuvant chemotherapy after surgery. The performance of the proposed algorithm is consistently ranked highly compared to the other classification algorithms. Conclusion: The statistical classification method for individualized treatment of diseases developed in this study is expected to play a critical role in developing safer and more effective therapies that replace one-size-fits-all drugs with treatments that focus on specific patient needs.