Data-driven approaches outperform random choices in multi-label classification with Naive Bayes.
problem Improving multi-label classification performance with Naive Bayes classifiers.
method Comparison of data-driven, a priori, and random approaches on 12 benchmark datasets.
result Data-driven methods significantly outperform random baselines on F1 scores and Subset Accuracy.
Explains LDA and QDA for binary and multiple classes.
problem Classification methods in statistical and probabilistic learning.
method Optimization of decision boundaries, estimation of parameters, relation to other methods.
result Equivalence of LDA and Fisher discriminant analysis.
Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes primary weakness - attribute independence - and improve the performance of the algo…
Improved Naive Bayes for text classification with small datasets.
problem Poor performance of Naive Bayes in small training datasets.
method Introducing a correlation factor to Naive Bayes estimator.
result Our method achieves better accuracy than traditional Naive Bayes.
The paper revisits discriminative vs. generative classifiers, showing naive Bayes requires fewer samples.
problem Comparing discriminative and generative classifiers in multiclass settings.
method Theoretical analysis and simulations of naive Bayes vs. logistic regression.
result Multiclass naive Bayes requires fewer samples to approach asymptotic error compared to logistic regression.
Paper introduces Generalized Naive Bayes for better data fitting.
problem Improving Naive Bayes for better data fitting.
method Developed new greedy and optimal algorithms for GNB.
result Proves GNB fits data at least as well as classical NB.
Improved Naive Bayes classifier with neural network models.
problem Limited complexity handling and independence assumption in Naive Bayes.
method Introducing Neural Naive Bayes and Neural Pooled Markov Chain models.
result Error rate reduced by 4.5 on IMDB dataset.
A nearly tight convex relaxation for sparse Naive Bayes features.
problem Feature selection in large-scale Naive Bayes classification.
method Proposes a convex relaxation for the combinatorial maximum-likelihood problem of feature selection in Naive Bayes.
result The convex relaxation bounds become tight as marginal feature contributions decrease, providing a nearly optimal solution.
The paper compares one-hot encoding to Naïve Bayes for categorical variables.
problem Incorrect one-hot encoding affects Naïve Bayes performance.
method Mathematical and experimental analysis of PoB vs. categorical Naïve Bayes.
result Posterior probabilities are usually greater in the PoB case, but agree on the maximum a posteriori class label.
Proposes a non-convex optimization method for a parsimonious weighted naive Bayes classifier.
problem Improving naïve Bayes classifier performance with a large number of input variables.
method Sparse regularization of model log-likelihood for direct estimation of variable weights.
result Optimization-based weighted naïve Bayes classifiers achieve equivalent performance to averaging-based classifiers.
Proposes a sparse Naïve Bayes classifier to improve performance and interpretability.
problem Naïve Bayes assumes feature independence, which is violated in real data.
method Integrates feature correlation and performance measures for feature selection.
result Competitive results in accuracy, sparsity, and running times for balanced datasets.
We develop a class of rules spanning the range between quadratic discriminant analysis and naive Bayes, through a path of sparse graphical models. A group lasso penalty is used to introduce shrinkage and encourage a similar pattern of sparsity across precision matrices. It gives sparse estimates of interactions and pro…
Naive Bayes can be used as a discriminative classifier, matching the definition of logistic regression.
problem The definition of generative and discriminative classifiers.
method Comparing Naive Bayes and logistic regression, showing they can be used in either generative or discriminative ways.
result Naive Bayes can be used as a discriminative classifier.
Draft proposes adapting neural networks to match naive Bayes classifiers.
problem Bridge between neural networks and naive Bayes classifiers.
method Class-conditional compression and disentanglement using variational bounds.
result Latent representations enable naive Bayes classifier performance.
Smart Bayes integrates generative and discriminative features for improved classification.
problem Improving classification performance by combining generative and discriminative modeling.
method Integrates generative likelihood-ratio features into a logistic-regression-style classifier.
result Often outperforms logistic regression and Naive Bayes in simulations and real data.
This paper proposes an efficient method for calculating Shapley values in Naive Bayes classifiers.
problem The need for explaining machine learning model decisions.
method An exact analytic expression of Shapley values for Naive Bayes classifiers.
result The proposed Shapley values provide informative results with low complexity and low computation time.
Two new Hie-TAN and Hie-TAN-Lite algorithms improve TAN for hierarchical feature spaces.
problem Learning dependencies in hierarchical feature spaces.
method Exploits hierarchical parent-child relationships as constraints to learn a dependency tree.
result Hie-TAN-Lite outperforms Hie-TAN and other methods in predictive accuracy.
Locally private Naive Bayes works for personal data.
problem Training Naive Bayes on personal data with privacy concerns.
method Local differential privacy, dimensionality reduction, and perturbation techniques.
result Naive Bayes accuracy maintained under local differential privacy.
Improved Naive Bayes for better phone call behavior classification.
problem Noise in mobile phone data affects phone call behavior classification accuracy.
method Improved naive Bayes classifier with behavioral pattern analysis and dynamic noise threshold.
result Our technique improves classification accuracy by 15%.
Fairness in Naive Bayes classifiers by identifying and eliminating discrimination patterns.
problem Ensuring fairness in machine learning models that use partial observations.
method Discover and eliminate discrimination patterns in naive Bayes classifiers through iterative learning.
result An algorithm that learns fair naive Bayes classifiers by removing discrimination patterns.
Optimal projections enhance Naive Bayes classification.
problem Improving Naive Bayes classification accuracy.
method Projection pursuit to find optimal linear projections.
result The approach substantially outperforms other models.
New algorithms explain Naive Bayes classifiers in polynomial time and delay.
problem Computing explanations for Naive Bayes classifiers efficiently.
method Developed log-linear time and polynomial delay algorithms for PI-explanations.
result Efficiently computed PI-explanations for linear classifiers.
Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDs), which are then used to reason about t…
We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators. Wrapper approaches guided by the NBC estimation of the classification error probability out-perform filter approaches while retaining …
In this paper we present a new Bayesian network model for classification that combines the naive-Bayes (NB) classifier and the finite-mixture (FM) classifier. The resulting classifier aims at relaxing the strong assumptions on which the two component models are based, in an attempt to improve on their classification pe…
Python tool creates machine-learning-ready solar dataset.
problem Creating a usable dataset for space weather forecasting.
method Python tool generates dataset from SoHO and SDO images, applying pre-processing.
result Dataset is machine-learning ready, free of missing data, and temporally synced.
In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers - Naive-Bayes, tree augmented Naive-Bayes, BN augmented Naive-Bayes and general BNs, where the latter two are learned using two variants of a conditional-independence (CI) based BN-learning algorithm. Experime…
XNB classifier improves model interpretability by selecting class-specific features.
problem Overfitting and poor model accuracy in high-dimensional datasets.
method XNB classifier uses Kernel Density Estimation and class-specific feature subsets.
result XNB classifier matches traditional Naive Bayes performance while improving interpretability.
Exact learning improves naive Bayes classifier performance for small samples.
problem Improving naive Bayes classifier performance with small sample sizes.
method Proposes an exact learning augmented naive Bayes classifier (ANB) that ensures a class variable with no parents.
result The proposed ANB method outperforms other methods in comparison experiments.
Defense against spam filter attacks using mixture models.
problem Data poisoning attacks on naive Bayes spam filters.
method Mixture of naive Bayes models to isolate attacks.
result Mixture model isolates attacks in a second component, preserving original spam.
Information planning speeds up text data learning with fewer examples.
problem Learning with limited training examples for text data.
method Entropy and mutual information-based information planning for three supervised models.
result Information planning accelerates learning for text data.
Paper presents a method for satellite imagery classification using texture features.
problem Object identification in urban areas using satellite imagery.
method Pixel-level study with various features (correlation, homogeneity, energy, contrast). Supervised classification using SVM and Naive Bayes.
result Naive Bayes outperforms SVM with an overall accuracy of 76%.
New method for high-dimensional linear regression using empirical Bayes.
problem Estimating prior in high-dimensional linear regression.
method Variational empirical Bayes approach with NPMLE and mean field approximation.
result Established asymptotic consistency and computational efficiency of the method.
Amobee's system won 3rd place in Twitter sentiment classification.
problem Sentiment detection on Twitter using deep learning.
method RNN models trained on a sentiment treebank, combined with classifiers.
result 3rd place in SemEval 2017 task 4, 5-label classification.
As a consequence of the strong and usually violated conditional independence assumption (CIA) of naive Bayes (NB) classifier, the performance of NB becomes less and less favorable compared to sophisticated classifiers when the sample size increases. We learn from this phenomenon that when the size of the training data …
We present a growing dimension asymptotic formalism. The perspective in this paper is classification theory and we show that it can accommodate probabilistic networks classifiers, including naive Bayes model and its augmented version. When represented as a Bayesian network these classifiers have an important advantage:…
Dynamic classifier chains improve multi-label classification efficiency.
problem Building efficient multi-label classification models.
method Dynamic ensemble of chain classifiers using Naive Bayes and nearest neighbor approaches, with heuristic for label order optimization.
result The proposed dynamic chain model based on Naive Bayes classifier and heuristic is efficient for multi-label classification.
Study analyzes IMDB movie comments and Twitter data using machine learning and vector space techniques.
problem Sentiment analysis of IMDB movie comments and Twitter data.
method Created a vector space in KNIME Analytics platform, used Decision Trees, Naïve Bayes, and SVM algorithms for classification.
result SVM algorithm provided the best classification results for both IMDB movie comments and Twitter data sets.
Frequently, acquiring training data has an associated cost. We consider the situation where the learner may purchase data during training, subject TO a budget. IN particular, we examine the CASE WHERE each feature label has an associated cost, AND the total cost OF ALL feature labels acquired during training must NOT e…
We describe two techniques that significantly improve the running time of several standard machine-learning algorithms when data is sparse. The first technique is an algorithm that effeciently extracts one-way and two-way counts--either real or expected-- from discrete data. Extracting such counts is a fundamental step…
The paper proposes using entropy to assess model confidence.
problem Discarded information in probability distributions hinders model confidence assessment.
method Entropy methods applied to assess model confidence.
result Theoretical explanation of confidence degradation in Naive Bayes model.
Naive Bayes model performs best in classifying seismological articles about precursory seismicity.
problem Classifying seismological articles about precursory seismicity using machine learning.
method Various supervised machine learning classifiers (Naive Bayes, k-Nearest Neighbors, Support Vector Machines, Random Forests) were tested on a seismological corpus of 100 articles.
result Naive Bayes model performs best with cross-validation accuracies of 86% for binary classification and up to 78% for multiclass classification.
This study examines how imbalanced training data affects author name disambiguation.
problem The impact of imbalanced training data on machine learning for author name disambiguation.
method Training three classifiers (Logistic Regression, Naïve Bayes, Random Forest) on multiple labeled datasets with various positive-negative training data ratios.
result Increasing negative training data can improve disambiguation performance but with diminishing returns.
New classifiers account for context-specific independences.
problem Restrictions in generative models for classification.
method Staged tree classifiers that account for context-specific independences.
result Staged tree classifiers achieve competitive classification accuracy.
Deep Bayes classifiers are more robust to adversarial attacks than discriminative classifiers.
problem Robustness of deep neural network classifiers against adversarial attacks.
method Developed deep Bayes classifier using conditional deep generative models and detection methods.
result Deep Bayes classifiers are more robust than deep discriminative classifiers.
Paper proves LCVB method's consistency in Bayesian posteriors and decision rules.
problem Approximating Bayesian posteriors and decision rules.
method Loss-calibrated variational Bayes (LCVB) method.
result LCVB method's consistency in both approximate posterior and decision rules.
NPMLE improves Gaussian denoising without prior knowledge of clusters.
problem Estimating Gaussian location mixtures from noisy data.
method Nonparametric Maximum Likelihood Estimator (NPMLE) for convex optimization.
result Empirical Bayes estimates perform nearly optimally in Gaussian denoising.
Study on the structure of classifier boundaries in DNA sequencing.
problem Understanding the structure of boundaries in a Bayes classifier for DNA sequencing.
method Examined the structure of the boundary in a Bayes classifier applied to DNA sequencing data. Introduced a new measure of uncertainty, Neighbor Similarity.
result The boundary is large and complex, and Neighbor Similarity effectively measures classifier uncertainty.