Instance Correlation Graph-based Naive Bayes

Chengyuan Li, Liangxiao Jiang, Wenjun Zhang, Liangjun Yu, Huan Zhang
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:35021-35033, 2025.

Abstract

Due to its simplicity, effectiveness and robustness, naive Bayes (NB) has continued to be one of the top 10 data mining algorithms. To improve its performance, a large number of improved algorithms have been proposed in the last few decades. However, in addition to Gaussian naive Bayes (GNB), there is little work on numerical attributes. At the same time, none of them takes into account the correlations among instances. To fill this gap, we propose a novel algorithm called instance correlation graph-based naive Bayes (ICGNB). Specifically, it first uses original attributes to construct an instance correlation graph (ICG) to represent the correlations among instances. Then, it employs a variational graph auto-encoder (VGAE) to generate new attributes from the constructed ICG and uses them to augment original attributes. Finally, it weights each augmented attribute to alleviate the attribute redundancy and builds GNB on the weighted attributes. The experimental results on tens of datasets show that ICGNB significantly outperforms its deserved competitors.Our codes and datasets are available at https://github.com/jiangliangxiao/ICGNB.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-li25ar, title = {Instance Correlation Graph-based Naive {B}ayes}, author = {Li, Chengyuan and Jiang, Liangxiao and Zhang, Wenjun and Yu, Liangjun and Zhang, Huan}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {35021--35033}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/li25ar/li25ar.pdf}, url = {https://proceedings.mlr.press/v267/li25ar.html}, abstract = {Due to its simplicity, effectiveness and robustness, naive Bayes (NB) has continued to be one of the top 10 data mining algorithms. To improve its performance, a large number of improved algorithms have been proposed in the last few decades. However, in addition to Gaussian naive Bayes (GNB), there is little work on numerical attributes. At the same time, none of them takes into account the correlations among instances. To fill this gap, we propose a novel algorithm called instance correlation graph-based naive Bayes (ICGNB). Specifically, it first uses original attributes to construct an instance correlation graph (ICG) to represent the correlations among instances. Then, it employs a variational graph auto-encoder (VGAE) to generate new attributes from the constructed ICG and uses them to augment original attributes. Finally, it weights each augmented attribute to alleviate the attribute redundancy and builds GNB on the weighted attributes. The experimental results on tens of datasets show that ICGNB significantly outperforms its deserved competitors.Our codes and datasets are available at https://github.com/jiangliangxiao/ICGNB.} }
Endnote
%0 Conference Paper %T Instance Correlation Graph-based Naive Bayes %A Chengyuan Li %A Liangxiao Jiang %A Wenjun Zhang %A Liangjun Yu %A Huan Zhang %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-li25ar %I PMLR %P 35021--35033 %U https://proceedings.mlr.press/v267/li25ar.html %V 267 %X Due to its simplicity, effectiveness and robustness, naive Bayes (NB) has continued to be one of the top 10 data mining algorithms. To improve its performance, a large number of improved algorithms have been proposed in the last few decades. However, in addition to Gaussian naive Bayes (GNB), there is little work on numerical attributes. At the same time, none of them takes into account the correlations among instances. To fill this gap, we propose a novel algorithm called instance correlation graph-based naive Bayes (ICGNB). Specifically, it first uses original attributes to construct an instance correlation graph (ICG) to represent the correlations among instances. Then, it employs a variational graph auto-encoder (VGAE) to generate new attributes from the constructed ICG and uses them to augment original attributes. Finally, it weights each augmented attribute to alleviate the attribute redundancy and builds GNB on the weighted attributes. The experimental results on tens of datasets show that ICGNB significantly outperforms its deserved competitors.Our codes and datasets are available at https://github.com/jiangliangxiao/ICGNB.
APA
Li, C., Jiang, L., Zhang, W., Yu, L. & Zhang, H.. (2025). Instance Correlation Graph-based Naive Bayes. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:35021-35033 Available from https://proceedings.mlr.press/v267/li25ar.html.

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