TY - GEN
T1 - Improving Usual Naive Bayes Classifier Performances with Neural Naïve Bayes based Models
AU - Azeraf, Elie
AU - Monfrini, Emmanuel
AU - Pieczynski, Wojciech
N1 - Publisher Copyright:
© 2022 by SCITEPRESS – Science and Technology Publications, Lda.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Naive Bayes is a popular probabilistic model appreciated for its simplicity and interpretability. However, the usual form of the related classifier suffers from two significant problems. First, as caring about the observations’ law, it cannot consider complex features. Moreover, it considers the conditional independence of the observations given the hidden variable. This paper introduces the original Neural Naive Bayes, modeling the classifier’s parameters induced from the Naive Bayes with neural network functions. This method allows for correcting the first default. We also introduce new Neural Pooled Markov Chain models, alleviating the conditional independence assumption. We empirically study the benefits of these models for Sentiment Analysis, dividing the error rate of the usual classifier by 4:5 on the IMDB dataset with the FastText embedding, and achieving an equivalent F1 as RoBERTa on TweetEval emotion dataset, while being more than a thousand times faster for inference.
AB - Naive Bayes is a popular probabilistic model appreciated for its simplicity and interpretability. However, the usual form of the related classifier suffers from two significant problems. First, as caring about the observations’ law, it cannot consider complex features. Moreover, it considers the conditional independence of the observations given the hidden variable. This paper introduces the original Neural Naive Bayes, modeling the classifier’s parameters induced from the Naive Bayes with neural network functions. This method allows for correcting the first default. We also introduce new Neural Pooled Markov Chain models, alleviating the conditional independence assumption. We empirically study the benefits of these models for Sentiment Analysis, dividing the error rate of the usual classifier by 4:5 on the IMDB dataset with the FastText embedding, and achieving an equivalent F1 as RoBERTa on TweetEval emotion dataset, while being more than a thousand times faster for inference.
KW - Bayes Classifier
KW - Naive Bayes
KW - Neural Naive Bayes
KW - Neural Pooled Markov Chain
KW - Pooled Markov Chain
U2 - 10.5220/0010890400003122
DO - 10.5220/0010890400003122
M3 - Conference contribution
AN - SCOPUS:85174564063
SN - 9789897585494
T3 - International Conference on Pattern Recognition Applications and Methods
SP - 315
EP - 322
BT - ICPRAM 2022 - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods, Volume 1
A2 - De Marsico, Maria
A2 - Sanniti di Baja, Gabriella
A2 - Fred, Ana L.N.
PB - Science and Technology Publications, Lda
T2 - 11th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2022
Y2 - 3 February 2022 through 5 February 2022
ER -