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.
The Mahalanobis distance estimates the distance between a point and a distribution. Furthermore, this measure does not comply with the interval result, [0, 1].
References
Cerri, R., de Carvalho, A.F.A.: Comparing local and global hierarchical multilabel classification methods using decision trees, January 2009
Chapelle, O., Schlkopf, B., Zien, A.: Semi-Supervised Learning1st edn. The MIT Press, Cambridge (2010)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
van Engelen, J.E., Hoos, H.H.: A survey on semi-supervised learning. Mach. Learn. 109(2), 373–440 (2019). https://doi.org/10.1007/s10994-019-05855-6
Metz, J., Freitas, A.A.: Extending hierarchical classification with semi-supervised learning. In: Proceedings of the UK Workshop on Computational Intelligence, pp. 1–6 (2009)
Nakano, F.K., Pinto, W.J., Pappa, G.L., Cerri, R.: Top-down strategies for hierarchical classification of transposable elements with neural networks. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2539–2546, May 2017
Santos, A., Canuto, A.: Applying semi-supervised learning in hierarchical multi-label classification. Expert Syst. Appl. 41(14), 6075–6085 (2014)
Santos, A., Canuto, A.: Applying the self-training semi-supervised learning in hierarchical multi-label methods. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 872–879 (2014)
Santos, A.M., Canuto, A.M.P.: Using semi-supervised learning in multi-label classification problems. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2012)
Serrano-Pérez, J., Sucar, L.E.: Hierarchical classification with Bayesian networks and chained classifiers. In: Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conference, Sarasota, Florida, USA, 19–22 May 2019, pp. 488–493 (2019)
Silla, C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Disc. 22(1), 31–72 (2011)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multilabel classification. IEEE Trans. Knowl. Data Eng. 23(7), 1079–1089 (2011)
Vens, C., Struyf, J., Schietgat, L., Džeroski, S., Blockeel, H.: Decision trees for hierarchical multi-label classification. Mach. Learn. 73(2), 185 (2008)
Xiao, H., Liu, X., Song, Y.: Efficient path prediction for semi-supervised and weakly supervised hierarchical text classification. In: The World Wide Web Conference, pp. 3370–3376. WWW 2019, Association for Computing Machinery, New York (2019)
Zhu, X.: Semi-supervised learning literature survey. Technical report, University of Wisconsin-Madison, July 2008
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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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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