Improving usual Naive Bayes classifier performances with Neural Naive Bayes based models

• URL: http://arxiv.org/abs/2111.07307v1
• Date: Sun, 14 Nov 2021 10:42:26 GMT
• Title: Improving usual Naive Bayes classifier performances with Neural Naive Bayes based models
• Authors: Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski
• Abstract summary: This paper introduces the original Neural Naive Bayes, modeling the parameters of the classifier induced from the Naive Bayes with neural network functions. We also introduce new Neural Pooled Markov Chain models, alleviating the independence condition.
• Score: 6.939768185086753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Naive Bayes is a popular probabilistic model appreciated for its simplicity and interpretability. However, the usual form of the related classifier suffers from two major 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 parameters of the classifier induced from the Naive Bayes with neural network functions. This allows to correct the first problem. We also introduce new Neural Pooled Markov Chain models, alleviating the independence condition. 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.

Related papers

• Simple and Interpretable Probabilistic Classifiers for Knowledge Graphs [0.0]
  We describe an inductive approach based on learning simple belief networks. We show how such models can be converted into (probabilistic) axioms (or rules)
  arXiv  Detail & Related papers  (2024-07-09T17:05:52Z)
• A Notion of Uniqueness for the Adversarial Bayes Classifier [0.0]
  We propose a new notion of uniqueness for the adversarial Bayes classifier in the setting of binary classification. We show that as the perturbation radius increases, certain the regularity of adversarial Bayes classifiers improves.
  arXiv  Detail & Related papers  (2024-04-25T18:10:27Z)
• Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison [0.0]
  Evidence Networks can enable Bayesian model comparison when state-of-the-art methods fail. We introduce the leaky parity-odd power transform, leading to the novel l-POP-Exponential'' loss function. We show that Evidence Networks are explicitly independent of dimensionality of the parameter space and scale mildly with the complexity of the posterior probability density function.
  arXiv  Detail & Related papers  (2023-05-18T18:14:53Z)
• Optimal partition of feature using Bayesian classifier [0.0]
  In Naive Bayes, certain features are called independent features as they have no conditional correlation or dependency when predicting a classification. We propose a novel technique called the Comonotone-Independence (CIBer) which is able to overcome the challenges posed by the Naive Bayes method.
  arXiv  Detail & Related papers  (2023-04-27T21:19:06Z)
• Neural Dependencies Emerging from Learning Massive Categories [94.77992221690742]
  This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories. 2) Neural dependencies exist not only within a single model, but even between two independently learned models.
  arXiv  Detail & Related papers  (2022-11-21T09:42:15Z)
• Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification [86.32752788233913]
  In classification problems, the Bayes error can be used as a criterion to evaluate classifiers with state-of-the-art performance. We propose a simple and direct Bayes error estimator, where we just take the mean of the labels that show emphuncertainty of the classes. Our flexible approach enables us to perform Bayes error estimation even for weakly supervised data.
  arXiv  Detail & Related papers  (2022-02-01T13:22:26Z)
• Evaluating State-of-the-Art Classification Models Against Bayes Optimality [106.50867011164584]
  We show that we can compute the exact Bayes error of generative models learned using normalizing flows. We use our approach to conduct a thorough investigation of state-of-the-art classification models.
  arXiv  Detail & Related papers  (2021-06-07T06:21:20Z)
• DoLFIn: Distributions over Latent Features for Interpretability [8.807587076209568]
  We propose a novel strategy for achieving interpretability in neural network models. Our approach builds on the success of using probability as the central quantity. We show that DoLFIn not only provides interpretable solutions, but even slightly outperforms the classical CNN and BiLSTM text classification.
  arXiv  Detail & Related papers  (2020-11-10T18:32:53Z)
• Explaining and Improving Model Behavior with k Nearest Neighbor Representations [107.24850861390196]
  We propose using k nearest neighbor representations to identify training examples responsible for a model's predictions. We show that kNN representations are effective at uncovering learned spurious associations. Our results indicate that the kNN approach makes the finetuned model more robust to adversarial inputs.
  arXiv  Detail & Related papers  (2020-10-18T16:55:25Z)
• Understanding Neural Abstractive Summarization Models via Uncertainty [54.37665950633147]
  seq2seq abstractive summarization models generate text in a free-form manner. We study the entropy, or uncertainty, of the model's token-level predictions. We show that uncertainty is a useful perspective for analyzing summarization and text generation models more broadly.
  arXiv  Detail & Related papers  (2020-10-15T16:57:27Z)
• Bayesian Deep Learning and a Probabilistic Perspective of Generalization [56.69671152009899]
  We show that deep ensembles provide an effective mechanism for approximate Bayesian marginalization. We also propose a related approach that further improves the predictive distribution by marginalizing within basins of attraction.
  arXiv  Detail & Related papers  (2020-02-20T15:13:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.