Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
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ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Stochastic Algorithm for Feature Selection in Pattern Recognition
The Journal of Machine Learning Research
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
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Feature selection in a kernel space
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HDKM '08 Proceedings of the second Australasian workshop on Health data and knowledge management - Volume 80
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ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
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Kernel-based feature extraction under maximum margin criterion
Journal of Visual Communication and Image Representation
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Future Generation Computer Systems
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We propose a series of new feature weighting algorithms, all stemming from a new interpretation of RELIEF as an online algorithm that solves a convex optimization problem with a margin-based objective function. The new interpretation explains the simplicity and effectiveness of RELIEF, and enables us to identify some of its weaknesses. We offer an analytic solution to mitigate these problems. We extend the newly proposed algorithm to handle multiclass problems by using a new multiclass margin definition. To reduce computational costs, an online learning algorithm is also developed. Convergence theorems of the proposed algorithms are presented. Some experiments based on the UCI and microarray datasets are performed to demonstrate the effectiveness of the proposed algorithms.