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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Direct integration of microarrays for selecting informative genes and phenotype classification
Information Sciences: an International Journal
Cancer classification using Rotation Forest
Computers in Biology and Medicine
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Microarray data classification based on ensemble independent component selection
Computers in Biology and Medicine
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A novel feature selection approach for biomedical data classification
Journal of Biomedical Informatics
Computers in Biology and Medicine
Mixture classification model based on clinical markers for breast cancer prognosis
Artificial Intelligence in Medicine
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Using fuzzy support vector machine network to predict low homology protein structural classes
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Expert Systems with Applications: An International Journal
Penalized principal component analysis of microarray data
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Mining microarray data to predict the histological grade of a breast cancer
Journal of Biomedical Informatics
Graph-Based model-selection framework for large ensembles
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Combining gene expression and interaction network data to improve kidney lesion score prediction
International Journal of Bioinformatics Research and Applications
WSEAS Transactions on Information Science and Applications
Biclustering-driven ensemble of Bayesian belief network classifiers for underdetermined problems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Feature weighting by RELIEF based on local hyperplane approximation
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Journal of Biomedical Informatics
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Microarray data analysis and classification has demonstrated convincingly that it provides an effective methodology for the effective diagnosis of diseases and cancers. Although much research has been performed on applying machine learning techniques for microarray data classification during the past years, it has been shown that conventional machine learning techniques have intrinsic drawbacks in achieving accurate and robust classifications. This paper presents a novel ensemble machine learning approach for the development of robust microarray data classification. Different from the conventional ensemble learning techniques, the approach presented begins with generating a pool of candidate base classifiers based on the gene sub-sampling and then the selection of a sub-set of appropriate base classifiers to construct the classification committee based on classifier clustering. Experimental results have demonstrated that the classifiers constructed by the proposed method outperforms not only the classifiers generated by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods (bagging and boosting).