Abstract
Developing models and methods to manage data vagueness is a current effervescent research field. Some work has been done with supervised problems but unsupervised problems and uncertainty have still not been studied. In this work, an extension of the Fuzzy Mutual Information Feature Selection algorithm for unsupervised problems is outlined. This proposal is a two stage procedure. Firstly, it makes use of the fuzzy mutual information measure and Battiti’s feature selection algorithm and of a genetic algorithm to analyze the relationships between feature subspaces in a high dimensional space. The second stage uses a simple ad hoc heuristic with the aim to extract the most relevant relationships. It is concluded, given the results from the experiments carried out in this preliminary work, that it is possible to apply frequent pattern mining or similar methods in the second stage to reduce the dimensionality of the data set.
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Villar, J.R., Suárez, M.R., Sedano, J., Mateos, F. (2009). Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_68
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DOI: https://doi.org/10.1007/978-3-642-02319-4_68
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