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A Blocking Strategy for Ranking Features According to Probabilistic Relevance

  • Gianluca BontempiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)

Abstract

The paper presents an algorithm to rank features in “small number of samples, large dimensionality” problems according to probabilistic feature relevance, a novel definition of feature relevance. Probabilistic feature relevance, intended as expected weak relevance, is introduced in order to address the problem of estimating conventional feature relevance in data settings where the number of samples is much smaller than the number of features. The resulting ranking algorithm relies on a blocking approach for estimation and consists in creating a large number of identical configurations to measure the conditional information of each feature in a paired manner. Its implementation can be made embarrassingly parallel in the case of very large n. A number of experiments on simulated and real data confirms the interest of the approach.

Keywords

Proposal Distribution Ranking Algorithm Feature Selection Technique Probabilistic Relevance Conditional Mutual Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The author acknowledges the support of the “BruFence: Scalable machine learning for automating defense system” project (RBC/14 PFS-ICT 5), funded by the Institute for the encouragement of Scientific Research and Innovation of Brussels (INNOVIRIS, Brussels Region, Belgium).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Machine Learning Group, Computer Science Department, Interuniversity Institute of Bioinformatics in Brussels (IB)2ULB, Université Libre de BruxellesBruxellesBelgium

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