Permutation-Based Diversity Measure for Classifier-Chain Approach

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)


In this paper, the problem of multilabel classification using the classifier chain scheme is addressed. We deal with the problem of building a diverse ensemble of the classifier-chain-based ensemble. For this purpose, we propose a permutation-based criterion of chain diversity. The final ensemble is build using a multi-objective genetic algorithm, which is used to optimise classification quality and chain diversity simultaneously. The proposed methods were evaluated using 29 benchmark datasets. The comparison was performed using four different multi-label evaluation measures. The experimental study reveals that the proposed approach provides a better classification quality than response-based diversity criteria.


Multi-label classification Classifier-chain Diversity 



This work was supported by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Science and Technology. Computational resources were provided by PL-Grid Infrastructure.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

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

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