Idiot's Bayes: Not So Stupid after All?

Hand, David J.; Yu, Keming

doi:10.2307/1403452

Cited by 279 publications

(304 citation statements)

References 20 publications

Supporting

Mentioning

297

Contrasting

Unclassified

Order By: Relevance

“…This last minimization is exactly the minimum spanning tree problem, and the argument that minimizes it is the same as the argument that maximizes (2). Because this algorithm has to initialize the Θ(n 2 ) edges between every pair of features and then to solve the minimum spanning tree (e.g.…”

Section: Classification and Learning Tansmentioning

confidence: 99%

“…Yet, at least under the zero-one accuracy, the naive Bayes classifier performs surprisingly well [1,2]. Reasons for this phenomenon have been provided, among others, by Friedman [3], who proposed an approach to decompose the misclassification error into bias error and variance error; the bias error represents how closely the classifier approximates the target function, while the variance error reflects the sensitivity of the parameters of the classifier to the training sample.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extended Tree Augmented Naive Classifier

Campos

Cuccu

Corani

et al. 2014

Probabilistic Graphical Models

View full text Add to dashboard Cite

Abstract. This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

show abstract

Section: Classification and Learning Tansmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extended Tree Augmented Naive Classifier

Campos

Cuccu

Corani

et al. 2014

Probabilistic Graphical Models

View full text Add to dashboard Cite

show abstract

“…Despite the fact that the independence assumption in the naïve Bayes classifier is almost always incorrect in real applications, many studies have shown a very good performance of the model, even in comparison with much more sophisticated classifiers(for example [4,6,11,21,22]). These results are of particular interest especially considering the many advantages of the naïve Bayes classifier in practical applications: it is very simple, very efficient and very easy to interpret and implement.…”

Section: Bayesian Network For Classificationmentioning

confidence: 99%

“…These results are of particular interest especially considering the many advantages of the naïve Bayes classifier in practical applications: it is very simple, very efficient and very easy to interpret and implement. Hand and Yu [11] give some mathematical justifications of why such a simple and unrealistic model might perform so well on future observations: simple models, like this, have a lower variance than more complex models. Hence, despite having a larger bias, they might perform better on observations outside the training data.…”

Section: Bayesian Network For Classificationmentioning

confidence: 99%

A spatio-temporal Bayesian network classifier for understanding visual field deterioration

Tucker

Vinciotti

Liu

et al. 2005

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the 'nasal step', an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data.

show abstract

“…The naïve Bayes classifier [31,32] is a probabilistic method for classification. It performs an approximate calculation of the probability that an example belongs to a class given the values of predictor variables.…”

Section: Naïve Bayesmentioning

confidence: 99%

Bayesian network multi-classifiers for protein secondary structure prediction

Robles

Larrañaga

Peña

et al. 2004

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research.In this work we present several multi-classifiers that combine the predictions of the best current classifiers available on Internet. Our results prove that combining the predictions of a set of classifiers by creating composite classifiers is a fruitful one. We have created multi-classifiers that are more accurate than any of the component classifiers. The multi-classifiers are based on Bayesian networks. They are validated with 9 different datasets. Their predictive accuracy results outperform the best secondary structure predictors by 1.21% on average.Our main contributions are: (i) we improved the best know predictive accuracy by 1.21%, (ii) our best results have been obtained with a new semi naïve Bayes approach named Pazzani-EDA and (iii) our multi-classifiers combine results of previously build classifiers predictions obtained through Internet, thanks to our development of a Java application.

show abstract

Idiot's Bayes: Not So Stupid after All?

Cited by 279 publications

References 20 publications

Extended Tree Augmented Naive Classifier

Extended Tree Augmented Naive Classifier

A spatio-temporal Bayesian network classifier for understanding visual field deterioration

Bayesian network multi-classifiers for protein secondary structure prediction

Contact Info

Product

Resources

About