Fractals everywhere
Machine Learning
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Characterization of Prokaryotic and Eukaryotic Promoters Using Hidden Markov Models
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
A neural network based multi-classifier system for gene identification in DNA sequences
Neural Computing and Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Feature selection with dynamic mutual information
Pattern Recognition
A filter based feature selection approach using lempel ziv complexity
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
A prototype classifier based on gravitational search algorithm
Applied Soft Computing
Spatial distance join based feature selection
Engineering Applications of Artificial Intelligence
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Feature selection methods can be classified broadly into filter and wrapper approaches. Filter-based methods filter out features which are irrelevant to the target concept by ranking each feature according to some discrimination measure and then select features with high ranking value. In this paper, a filter-based feature selection method based on correlation fractal dimension (CFD) discrimination measure is proposed. One of the subgoals of this paper is to outline some issues that arise while calculating fractal dimension for higher dimensional 'empirical' data sets. It is well known that the calculation of fractal dimension for empirical data sets is meaningful only for an appropriate range of scales. We demonstrate through experimentation on data sets of various sizes that fractal dimension-based algorithms cannot be applied routinely to higher dimensional data sets as the calculation of fractal dimension is inherently sensitive to parameters like range of scales and the size of the data sets. Based on the empirical analysis, we propose a new feature selection technique using CFD that avoids the above issues. We successfully applied the proposed algorithm on a challenging classification problem in bioinformatics, namely, Promoter Recognition.