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Feature Subsect Selection is an important issue in machine learning, since non-representative features may reduce accuracy and comprehensibility of hypotheses induced by supervised learning algorithms. Feature Subsect Selection is applied as data pre-processing step, which aims to find a subset of features that describes well the data to be used as input to the inducer. Several approaches to this problem have been proposed, among them the filter approach. This work proposes a filter which uses Fractal Dimension as importance criterion to select a subset of features from the original data. Empirical results on real world data sets are presented. Performance comparison of the proposed criterion with two other criteria frequently considered within the filter approach shows that Fractal Dimension is an appropriated criteria to select features for supervised learning.
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