The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
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
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Computational Statistics & Data Analysis
Methodological Review: Towards knowledge-based gene expression data mining
Journal of Biomedical Informatics
Comparison of classification accuracy using Cohen's Weighted Kappa
Expert Systems with Applications: An International Journal
Cancer classification using Rotation Forest
Computers in Biology and Medicine
A novel ensemble of classifiers for microarray data classification
Applied Soft Computing
Patient-centered yes/no prognosis using learning machines
International Journal of Data Mining and Bioinformatics
A model-free ensemble method for class prediction with application to biomedical decision making
Artificial Intelligence in Medicine
Microarray data classification based on ensemble independent component selection
Computers in Biology and Medicine
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A novel ensemble machine learning for robust microarray data classification
Computers in Biology and Medicine
Mixture classification model based on clinical markers for breast cancer prognosis
Artificial Intelligence in Medicine
AIBench: A rapid application development framework for translational research in biomedicine
Computer Methods and Programs in Biomedicine
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
International Journal of Data Mining and Bioinformatics
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Since the introduction of DNA microarray technology, there has been an increasing interest on clinical application for cancer diagnosis. However, in order to effectively translate the advances in the field of microarray-based classification into the clinic area, there are still some problems related with both model performance and biological interpretability of the results. In this paper, a novel ensemble model is proposed able to integrate prior knowledge in the form of gene sets into the whole microarray classification process. Each gene set is used as an informed feature selection subset to train several base classifiers in order to estimate their accuracy. This information is later used for selecting those classifiers comprising the final ensemble model. The internal architecture of the proposed ensemble allows the replacement of both base classifiers and the heuristics employed to carry out classifier fusion, thereby achieving a high level of flexibility and making it possible to configure and adapt the model to different contexts. Experimental results using different datasets and several gene sets show that the proposal is able to outperform classical alternatives by using existing prior knowledge adapted from publicly available databases.