Recursive autonomy identification-based learning of augmented naive Bayes classifiers

Shouta Sugahara, Wakaba Kishida, Koya Kato, Maomi Ueno
Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:265-276, 2022.

Abstract

Earlier reports have described classification accuracies of exactly learned augmented naive Bayes (ANB) classifiers. Those results indicate that a class variable with no parent has higher accuracy than those of other Bayesian network classifiers. Additionally, asymptotic estimation of the class posterior identical to that of the exactly learned Bayesian network is guaranteed to be achieved. Nevertheless, exact learning of large ANB is difficult because it entails an associated NP-hard problem that worsens as the number of variables increases. Recent reports have described that constraint-based learning methods with Bayes factor achieve larger network structures than when using traditional methods. This study proposes an efficient learning algorithm of an ANB classifier using recursive autonomy identification (RAI) with Bayes factor. A unique benefit of the proposed method is that the proposed method is guaranteed to accelerate execution of the RAI algorithm when the data follow an ANB structure. Numerical experiments were conducted to demonstrate the effectiveness of the proposed method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v186-sugahara22a, title = {Recursive autonomy identification-based learning of augmented naive Bayes classifiers}, author = {Sugahara, Shouta and Kishida, Wakaba and Kato, Koya and Ueno, Maomi}, booktitle = {Proceedings of The 11th International Conference on Probabilistic Graphical Models}, pages = {265--276}, year = {2022}, editor = {Salmerón, Antonio and Rumı́, Rafael}, volume = {186}, series = {Proceedings of Machine Learning Research}, month = {05--07 Oct}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v186/sugahara22a/sugahara22a.pdf}, url = {https://proceedings.mlr.press/v186/sugahara22a.html}, abstract = {Earlier reports have described classification accuracies of exactly learned augmented naive Bayes (ANB) classifiers. Those results indicate that a class variable with no parent has higher accuracy than those of other Bayesian network classifiers. Additionally, asymptotic estimation of the class posterior identical to that of the exactly learned Bayesian network is guaranteed to be achieved. Nevertheless, exact learning of large ANB is difficult because it entails an associated NP-hard problem that worsens as the number of variables increases. Recent reports have described that constraint-based learning methods with Bayes factor achieve larger network structures than when using traditional methods. This study proposes an efficient learning algorithm of an ANB classifier using recursive autonomy identification (RAI) with Bayes factor. A unique benefit of the proposed method is that the proposed method is guaranteed to accelerate execution of the RAI algorithm when the data follow an ANB structure. Numerical experiments were conducted to demonstrate the effectiveness of the proposed method.} }
Endnote
%0 Conference Paper %T Recursive autonomy identification-based learning of augmented naive Bayes classifiers %A Shouta Sugahara %A Wakaba Kishida %A Koya Kato %A Maomi Ueno %B Proceedings of The 11th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2022 %E Antonio Salmerón %E Rafael Rumı́ %F pmlr-v186-sugahara22a %I PMLR %P 265--276 %U https://proceedings.mlr.press/v186/sugahara22a.html %V 186 %X Earlier reports have described classification accuracies of exactly learned augmented naive Bayes (ANB) classifiers. Those results indicate that a class variable with no parent has higher accuracy than those of other Bayesian network classifiers. Additionally, asymptotic estimation of the class posterior identical to that of the exactly learned Bayesian network is guaranteed to be achieved. Nevertheless, exact learning of large ANB is difficult because it entails an associated NP-hard problem that worsens as the number of variables increases. Recent reports have described that constraint-based learning methods with Bayes factor achieve larger network structures than when using traditional methods. This study proposes an efficient learning algorithm of an ANB classifier using recursive autonomy identification (RAI) with Bayes factor. A unique benefit of the proposed method is that the proposed method is guaranteed to accelerate execution of the RAI algorithm when the data follow an ANB structure. Numerical experiments were conducted to demonstrate the effectiveness of the proposed method.
APA
Sugahara, S., Kishida, W., Kato, K. & Ueno, M.. (2022). Recursive autonomy identification-based learning of augmented naive Bayes classifiers. Proceedings of The 11th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 186:265-276 Available from https://proceedings.mlr.press/v186/sugahara22a.html.

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