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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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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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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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A Fundamental Issue of Naive Bayes
2003Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. But the conditional independence assumption on which it is based, is rarely true in real-world applications. Researchers extended naive Bayes to represent dependence explicitly, and proposed related learning algorithms based on ...
Harry Zhang, Charles X. Ling
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2021
With the help of the Naive Bayes classifier, this master thesis attempts to generate a trading strategy that outperforms the returns of two selected stock markets, the German (DAX) and the American (S&P 500) stock market, over a period ranging from 2004 to 2019. Trading decisions are made based on the a-posteriori probabilities derived based on the
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With the help of the Naive Bayes classifier, this master thesis attempts to generate a trading strategy that outperforms the returns of two selected stock markets, the German (DAX) and the American (S&P 500) stock market, over a period ranging from 2004 to 2019. Trading decisions are made based on the a-posteriori probabilities derived based on the
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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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