Abstract
Unsupervised Feature Selection is an area of research that currently has received much attention in the scientific community due to its wide application in practical problems where unlabeled data arise. One of these problems is profiling the structure of bacterial communities in the oceans, where it is required to identify and select relevant features from unlabeled marine sediment samples. This paper introduces a methodology to identify and select a set of relevant features in this field. To select a subset of relevant features, we rely on a synergy between ranking-based unsupervised feature selection methods, an introduced internal validation index, and a clustering algorithm. According to the results obtained in our analyses, the proposed methodology can select those features that best discover cluster structures in this kind of data.
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Notes
- 1.
A feature is consistent if it takes similar values for the objects that are close to each other and dissimilar values for far apart objects.
- 2.
Maximum or minimum, depending on the internal evaluation index used.
- 3.
The value computed by these indices increases or decreases monotonically regarding the number of features.
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Acknowledgements
The first author gratefully acknowledges the Instituto Nacional de Astrofósica, Óptica y Electrínica (INAOE) for the collaboration grant awarded for developing this research. We also thank E. Ernestina Godoy-Lozano and collaborators to provide the data for the analysis presented in this paper.
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Solorio-Fernández, S., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2021). Unsupervised Feature Selection Methodology for Analysis of Bacterial Taxonomy Profiles. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_5
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