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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).
null Liangxiao Jiang +2 more
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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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2020
The data mining methods we have been learning all favor numerical data: linear regression, LDA, k-means clustering, logistic regression, and K-NN. Naive Bayes favors categorical data, however. Because of its simplicity, naive Bayes data mining method is much more efficient compared to other data mining methods, while its performance can still match ...
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The data mining methods we have been learning all favor numerical data: linear regression, LDA, k-means clustering, logistic regression, and K-NN. Naive Bayes favors categorical data, however. Because of its simplicity, naive Bayes data mining method is much more efficient compared to other data mining methods, while its performance can still match ...
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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.
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Generalized Naive Bayes Classifiers
ACM SIGKDD Explorations Newsletter, 2005This paper presents a generalization of the Naive Bayes Classifier. The method is specifically designed for binary classification problems commonly found in credit scoring and marketing applications. The Generalized Naive Bayes Classifier turns out to be a powerful tool for both exploratory and predictive analysis.
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Naive Feature Selection: Sparsity in Naive Bayes
2020Due 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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Selecting the optimal immunotherapy regimen in driver-negative metastatic NSCLC
Nature Reviews Clinical Oncology, 2021Roy S Herbst, Sarah B Goldberg
exaly