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Augmenting naive Bayes for ranking
Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005 Naive Bayes is an effective and efficient learning algorithm in classification. In many applications, however, an accurate ranking of instances based on the class probability is more desirable. Unfortunately, naive Bayes has been found to produce poor probability estimates.
Harry Zhang, Liangxiao Jiang, Jiang Su
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A Novel Bayes Model: Hidden Naive Bayes
IEEE Transactions on Knowledge and Data Engineering, 2009Because learning an optimal Bayesian network classifier is an NP-hard problem, learning-improved naive Bayes has attracted much attention from researchers. In this paper, we summarize the existing improved algorithms and propose a novel Bayes model: hidden naive Bayes (HNB).
Liangxiao Jiang +2 more
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Naive Bayes for optimal ranking
Journal of Experimental and Theoretical Artificial Intelligence, 2008It is well known that naive Bayes performs surprisingly well in classification, but its probability estimation is poor. AUC (the area under the receiver operating characteristics curve) is a measure different from classification accuracy and probability estimation, which is often used to measure the quality of rankings.
Harry Zhang, Jiang Su
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Naive Feature Selection: Sparsity in Naive Bayes.
CoRR, 2019 Due to its linear complexity, naive Bayes classification remains an attractive supervised learning method, especially in very large-scale settings. We propose a sparse version of naive Bayes, which can be used for feature selection. This leads to a combinatorial maximum-likelihood problem, for which we provide an exact solution in the case of binary ...
Askari, Armin +2 more
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Collaboratively weighted naive Bayes
Knowledge and Information Systems, 2021Naive Bayes (NB) was once awarded as one of the top 10 data mining algorithms, but the unreliable probability estimation and the unrealistic attribute conditional independence assumption limit its performance. To alleviate these two primary weaknesses simultaneously, instance and attribute weighting has been recently proposed.
Huan Zhang 0007 +2 more
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Bayes’ Theorem and Naive Bayes Classifier
, 2019 The goal of this article is to give a mathematically rigorous yet easily accessible introduction to Bayes’ theorem and the foundations of naive Bayes learning. Starting from fundamental elements of probability theory, this text outlines all steps leading
Berrar, Daniel, Daniel Berrar
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24th Pan-Hellenic Conference on Informatics, 2020
Over the years, many learners that take advantage of the Bayesian theory have been developed and proved to be both efficient and performant in terms of classification predictiveness. Hidden Naive Bayes is no exception since its polynomial complexity makes it a desired base classifier to conduct under Weakly Supervised Learning that, unlikely the ...
Vangjel Kazllarof, Sotiris B. Kotsiantis
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Over the years, many learners that take advantage of the Bayesian theory have been developed and proved to be both efficient and performant in terms of classification predictiveness. Hidden Naive Bayes is no exception since its polynomial complexity makes it a desired base classifier to conduct under Weakly Supervised Learning that, unlikely the ...
Vangjel Kazllarof, Sotiris B. Kotsiantis
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Why the Naive Bayes approximation is not as Naive as it appears
2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), 2015The Naive Bayes approximation and associated classifier is widely used in machine learning and data mining and offers very robust performance across a large spectrum of problem domains. As it depends on a very strong assumption — independence among features — this has been somewhat puzzling.
Christopher R. Stephens +2 more
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The Learnability of Naive Bayes
2000Naive Bayes is an efficient and effective learning algorithm, but previous results show that its representation ability is severely limited since it can only represent certain linearly separable functions in the binary domain. We give necessary and sufficient conditions on linearly separable functions in the binary domain to be learnable by Naive Bayes
Huajie Zhang +2 more
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