Abstract
This work presents a new ranking method inspired on PageRank to reduce the dimensionality of the feature space in supervised classification problems. More precisely, as it relies on a weighted directed graph, it is ultimately inspired on TextRank, a PageRank based method that adds weights to the edges to express the strength of the connections between nodes. The method is based on dividing each original feature used to describe the training set into a set of fuzzy predicates and then ranking all of them by their ability to differentiate among classes in the light of this training set. The fuzzy predicates with the best scores can be then used as new features, replacing the original ones. The novelty of the proposal relies on being an approach halfway between feature selection and feature extraction approaches, being able to improve the discrimination ability of the original features but preserving the interpretability of the new features in the sense that they are fuzzy expressions. Preliminary results supports the suitability of the proposal.
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- 1.
In case of categorical features, each value of the feature will be represented by a fuzzy singleton.
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Acknowledgments
This work has been co-funded by the grants to research groups, IB16048 Proyect (Government of Extremadura), and the NanoSen-AQM Project (SOE2/P1/E0569).
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Carmona, P., Castro, J.L., Lozano, J., Suárez, J.I. (2018). A PageRank-Based Method to Extract Fuzzy Expressions as Features in Supervised Classification Problems. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_15
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DOI: https://doi.org/10.1007/978-3-030-00374-6_15
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