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A correlation-based feature weighting filter for multi-label Naive Bayes

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Abstract

Multi-label classification is used to solve the problem where multiple labels are associated with single sample. Naive Bayes (NB) classifier is widely used for single label classification due to its high performance and simplicity. Therefore it is vital to extend NB for multi-label classification. In single label classification feature weighted NB gives high accuracy by solving the conditional independence assumption of NB. However, NB is not much explored for multi-label classification. This paper proposes correlation dependent feature weighted NB (MLCFWNB) for multi-label classification. The proposed MLCFWNB is tested over eight benchmark datasets. The experimental result suggest that MLCFWNB wins 60% times in case of different multi-label learning evaluation parameters.

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Data availability

The data supporting the findings of this study are available in the http://mulan.sourceforge.net/datasets-mlc.html.

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Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Raipur, Raipur, India

    Gurudatta Verma & Tirath Prasad Sahu

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  1. Gurudatta Verma
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  2. Tirath Prasad Sahu
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Correspondence to Gurudatta Verma.

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Verma, G., Sahu, T.P. A correlation-based feature weighting filter for multi-label Naive Bayes. Int. j. inf. tecnol. 16, 611–619 (2024). https://doi.org/10.1007/s41870-023-01555-6

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