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Semi-supervised Hierarchical Classification Based on Local Information

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Advances in Artificial Intelligence – IBERAMIA 2022 (IBERAMIA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13788))

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Abstract

In this work, a semi-supervised hierarchical classifier based on local information (SSHC-BLI) is proposed. SSHC-BLI is a semi-supervised learning algorithm that can be applied to hierarchical classification, that is, it can handle labeled and unlabeled data in scenarios where the labels are arranged in a hierarchical structure. SSHC-BLI tries to pseudo-label each unlabeled instance using information of its nearest labeled instances. It uses a similarity function to determine whether the unlabeled instance is similar to its nearest labeled instances to assign it a label; if it is not, then it continues unlabeled. A heuristic similarity function of an instance with a set of instances was proposed to determine similitude. The method was tested in several datasets from functional genomics and compared against a hierarchical supervised classifier and two state of the art methods, showing in most cases superior performance, with statistical significance in accuracy and hierarchical F-measure.

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Notes

  1. 1.

    The Mahalanobis distance estimates the distance between a point and a distribution. Furthermore, this measure does not comply with the interval result, [0, 1].

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Acknowledgements

This work was sponsored in part by CONACYT, project A1-S-43346. J. Serrano-Pérez acknowledges the support from CONACYT scholarship number (CVU) 84075.

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Correspondence to Jonathan Serrano-Pérez .

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Serrano-Pérez, J., Sucar, L.E. (2022). Semi-supervised Hierarchical Classification Based on Local Information. In: Bicharra Garcia, A.C., Ferro, M., Rodríguez Ribón, J.C. (eds) Advances in Artificial Intelligence – IBERAMIA 2022. IBERAMIA 2022. Lecture Notes in Computer Science(), vol 13788. Springer, Cham. https://doi.org/10.1007/978-3-031-22419-5_22

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  • DOI: https://doi.org/10.1007/978-3-031-22419-5_22

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