Machine Learning
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval
Information Retrieval
Stability of feature selection algorithms: a study on high-dimensional spaces
Knowledge and Information Systems
Comparing Combination Rules of Pairwise Neural Networks Classifiers
Neural Processing Letters
Robust Feature Selection Using Ensemble Feature Selection Techniques
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
International Journal of Data Mining and Bioinformatics
Feature cluster selection for high-throughput data analysis
International Journal of Data Mining and Bioinformatics
Gene identification and survival prediction with Lp Cox regression and novel similarity measure
International Journal of Data Mining and Bioinformatics
Cross-platform microarray data integration using the Normalised Linear Transform
International Journal of Data Mining and Bioinformatics
Improving stability of feature selection methods
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Bioinformatics
Recursive Mahalanobis Separability Measure for Gene Subset Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust Feature Selection for Microarray Data Based on Multicriterion Fusion
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Dynamic classifier integration method
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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Feature ranking, which ranks features via their individual importance, is one of the frequently used feature selection techniques. Traditional feature ranking criteria are apt to produce inconsistent ranking results even with light perturbations in training samples when applied to high dimensional and small-sized gene expression data, which brings troubles for further studies such as biomarker identification. A widely used strategy for solving the inconsistencies is the multicriterion combination, where score normalisation is crucial. In this paper, three problems in existing methods are first analyzed, and then a new feature importance transformation algorithm based on resampling and permutation is proposed for score normalisation. Experimental studies on four popular gene expression data sets show that the multi-criterion combination based on the proposed score normalisation produces gene rankings with improved robustness.