Huei Diana Lee,  Maria Carolina Monard,  Feng Chung Wu. 

Feature Selection is a central problem in machine learning, as nonrelevant or redundant features may reduce accuracy and comprehensibility of hypothesis induced by supervised learning algorithms. Most of the state-of-art feature selection algorithms mainly focus on finding relevant features. However, it has been shown that relevance alone is not sufficient to select important features. Different approaches have been proposed to select features, among them the filter approach. This work proposes a filter that decouples relevance and redundancy analysis and introduces the use of Fractal Dimension to deal with redundant features. Empirical results on real world data show that Fractal Dimension is an appropriate criterion to filter out redundant features for supervised learning.

http://www.lbd.dcc.ufmg.br/colecoes/enia/2005/016.pdf

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