A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Beyond uniformity and independence: analysis of R-trees using the concept of fractal dimension
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
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Data mining: practical machine learning tools and techniques with Java implementations
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
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C4.5: Programs for Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
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PROPOR '08 Proceedings of the 8th international conference on Computational Processing of the Portuguese Language
Multi-objective genetic algorithm evaluation in feature selection
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Spatial distance join based feature selection
Engineering Applications of Artificial Intelligence
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Feature selection plays an important role in machine learning and is often applied as a data pre-processing step. Its objective is to choose a subset from the original set of features that describes a data set, according to some importance criterion, by removing irrelevant and/or redundant features, as they may decrease data quality and reduce the comprehensibility of hypotheses 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. It is also important to deal with the problem of features’ redundancy. For the purpose of selecting features and discarding others, it is necessary to measure the features’ goodness (importance), and many importance measures have been proposed. This work proposes a filter algorithm that decouples relevance and redundancy analysis, and introduces the use of Fractal Dimension to deal with redundant features. Empirical results on several data sets show that Fractal Dimension is an appropriate criterion to filter out redundant features for supervised learning.