Publication details

On Stopping Rules in Dependency-Aware Feature Ranking

Conference Paper (international conference)

Somol Petr, Grim Jiří, Filip Jiří, Pudil P.

serial: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, p. 286-293

action: CIARP 2013, Iberoamerican Congress on Pattern Recognition /18./, (Havana, CU, 20.11.2013-23.11.2013)

project(s): GAP103/11/0335, GA ČR

keywords: dimensionality reduction, feature selection, randomization and stopping rule

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abstract (eng):

Feature Selection in very-high-dimensional or small sample problems is particularly prone to computational and robustness complications. It is common to resort to feature ranking approaches only or to randomization techniques. A recent novel approach to the randomization idea in form of Dependency-Aware Feature Ranking (DAF) has shown great potential in tackling these problems well. Its original definition, however, leaves several technical questions open. In this paper we address one of these questions: how to define stopping rules of the randomized computation that stands at the core of the DAF method. We define stopping rules that are easier to interpret and show that the number of randomly generated probes does not need to be extensive.