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
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Stability of feature selection algorithms: a study on high-dimensional spaces
Knowledge and Information Systems
Stable feature selection via dense feature groups
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Consensus group stable feature selection
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving stability of feature selection methods
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
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Stability of feature selection is an important issue in knowledge discovery from high-dimensional data. A key factor affecting the stability of a feature selection algorithm is the sample size of training set. To alleviate the problem of small sample size in high-dimensional data, we propose a novel framework of margin based sample weighting which extensively explores the available samples. Specifically, it exploits the discrepancy among local profiles of feature importance at various samples and weights a sample according to the outlying degree of its local profile of feature importance. We also develop an efficient algorithm under the framework. Experiments on a set of public microarray datasets demonstrate that the proposed algorithm is effective at improving the stability of state-of-the-art feature selection algorithms, while maintaining comparable classification accuracy on selected features.