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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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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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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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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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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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Augmenting naive Bayes for ranking
Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005Naive 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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Probabilistic Fuzzy Naive Bayes
2015 Brazilian Conference on Intelligent Systems (BRACIS), 2015Bayesian networks are probabilistic graphical models capable of modeling statistical uncertainty and are widely applied in many classification problems. Specifically, Naive Bayesian networks are largely used due to their simple, naive structure, while still producing precise results.
Gabriel Moura, Mauro Roisenberg
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2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 2018
Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy, but the assumption of independence for attributes in NB is rarely true in reality. Attribute weighting is effective for overcoming the unrealistic assumption in NB, but it has received less attention than it warrants.
Liangjun Yu +3 more
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Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy, but the assumption of independence for attributes in NB is rarely true in reality. Attribute weighting is effective for overcoming the unrealistic assumption in NB, but it has received less attention than it warrants.
Liangjun Yu +3 more
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A novel Naive Bayes model: Packaged Hidden Naive Bayes
2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, 2011Naive Bayes classifier has good performance on many datasets, however, the performance is very poor on some datasets which have a strong correlation between attributes due to the conditional independence assumption is not always true in the real world. In the latest Hidden Naive Bayes (HNB) algorithm, each attribute corresponds to a hidden parent which
Yaguang Ji, Songnian Yu, Yafeng Zhang
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1999
Naive Bayes is a well known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. In this paper we present an iterative approach to naive Bayes. The iterative Bayes begins with the distribution tables built by the naive Bayes.
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Naive Bayes is a well known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. In this paper we present an iterative approach to naive Bayes. The iterative Bayes begins with the distribution tables built by the naive Bayes.
openaire +2 more sources