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Dynamic Classifier Chains for Multi-label Learning

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11824)

Abstract

In this paper, we deal with the task of building a dynamic ensemble of chain classifiers for multi-label classification. To do so, we proposed two concepts of the classifier chain algorithms that are able to change the label order of the chain without rebuilding the entire model. Such models allow anticipating the instance-specific chain order without the significant increase in the computational burden. The proposed chain models are built using the Naive Bayes classifier and nearest neighbour approaches. To take the benefits of the proposed algorithms, we developed a simple heuristic that allows the system to find relatively good label order. The experimental results showed that the proposed models and the heuristic are efficient tools for building dynamic chain classifiers.

Keywords

Multi-label Classifier chains Naive Bayes Dynamic chains Nearest neighbour 

Notes

Acknowledgments

This work is financed from Grant For Young Scientists and PhD Students Development, under agreement: 0402/0109/18.

Supplementary material

480714_1_En_40_MOESM1_ESM.pdf (117 kb)
Supplementary material 1 (pdf 116 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of Science and TechnologyWroclawPoland

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