Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Introduction to Algorithms
An introduction to variable and feature selection
The Journal of Machine Learning Research
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Robust SVM-based biomarker selection with noisy mass spectrometric proteomic data
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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This paper proposes a novel feature selection technique for SELDITOF spectrum data. The new technique, called RISC (Relevance Index by Sample Counting), measures the relevance of features based on each sample's discriminating power to partition the samples in the opposite class. We also proposes a heuristic searching method to obtain the optimal feature set, which makes use of the relevance parameters. Our technique is fast even for extremely high-dimensional datasets such as SELDI spectrum, since it has low computational complexity and consists of simple counting operations. The new technique also shows good performance comparable to the conventional feature selection techniques from the experiment on three clinical datasets from NCI/CCR and FDA/CBER Clinical Proteomics Program Databank: Ovarian 4-3-02, Ovarian 7-8-02, Prostate.