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Weighted naïve Bayes text classification algorithm based on improved distance correlation coefficient

  • S.I: Cognitive-inspired Computing and Applications
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Abstract

This paper proposes an innovative method to improve the attribute weighting approaches for naïve Bayes text classifiers using the improved distance correlation coefficient. The resulted model is called improved distance correlation coefficient attribute weighted multinomial naïve Bayes, denoted by IDCWMNB. Unlike the traditional correlation statistical measurements that consider the cumulative distribution function of random vectors, the improved distance correlation coefficient tests the joint correlation of random vectors by describing the distance between the joint characteristic function and the product of the marginal characteristic functions. Specifically, a measurement of inverse document frequency that considers the distribution information of document concentrating and scattering has been proposed. Then, the measurement and the distance correlation coefficient between attributes and categories have been combined to measure the importance of attributes to categories, to allocate different weights to different terms. Meanwhile, the learned attribute weights are incorporated into the posterior probability estimates of the multinomial naïve Bayes model, which is known as deep attribute weighting. This measurement is more effective than the traditional statistical measurements in the presence of nonlinear relationship between two random vectors. Experimental results taking benchmark and real-world data indicate that the new attribute weighting method can achieve an effective balance between classification accuracy and execution time.

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Acknowledgements

The authors would like to thank the anonymous reviewers and the editors for their valuable comments and suggestions. The authors would like to thank Chaoguo Tang, the chief engineer in China Railway Erju Group, for providing the real engineering geological survey text data.

Funding

This work is supported by the National Key R&D Program of China (2018YFC1503705), Science and Technology Research Project of Hubei Provincial Department of Education (B2017597).

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Authors and Affiliations

  1. Institute of Mathematics and Computer Science, Wuhan Textile University, Wuhan, 430073, China

    Shufen Ruan & Kunfang Song

  2. School of Mathematics and Physics, China University of Geosciences, Wuhan, 430074, China

    Shufen Ruan, Baozhou Chen & Hongwei Li

  3. Hubei Subsurface Multi-scale Imaging Key Laboratory, China University of Geosciences, Wuhan, 430074, China

    Shufen Ruan, Baozhou Chen & Hongwei Li

Authors
  1. Shufen Ruan
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  2. Baozhou Chen
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  3. Kunfang Song
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  4. Hongwei Li
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Correspondence to Shufen Ruan or Hongwei Li.

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Ruan, S., Chen, B., Song, K. et al. Weighted naïve Bayes text classification algorithm based on improved distance correlation coefficient. Neural Comput & Applic 34, 2729–2738 (2022). https://doi.org/10.1007/s00521-021-05989-6

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