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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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Learning an Optimal Naive Bayes Classifier
18th International Conference on Pattern Recognition (ICPR'06), 2006The naive Bayes classifier is an efficient classification model that is easy to learn and has a high accuracy in many domains. However, it has two main drawbacks: (i) its classification accuracy decreases when the attributes are not independent, and (ii) it can not deal with nonparametric continuous attributes.
M. Martinez-Arroyo, L.E. Sucar
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Fake news Detection Using Naive Bayes Classifier
Journal of Management and Service Science (JMSS), 2022Fake news has been on the rise thanks to rapid digitalization across all platforms and mediums. Many governments throughout the world are attempting to address this issue. The use of Natural Language Processing and Machine Learning techniques to properly identify fake news is the subject of this research.
Rahul Srivastava, Pawan Singh
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An Implementation of Naive Bayes Classifier
2018 International Conference on Computational Science and Computational Intelligence (CSCI), 2018The necessity of classification is highly demanded in real life. As a mathematical classification approach, the Naive Bayes classifier involves a series of probabilistic computations for the purpose of finding the best-fitted classification for a given piece of data within a problem domain.
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Bangla news classification using naive Bayes classifier
16th Int'l Conf. Computer and Information Technology, 2014Web is gigantic and being constantly update. Bangla news in web are rapidly grown in the era of information age where each news site has its own different layout and categorization for grouping news. These heterogeneity of layout and categorization can not always satisfy individual user's need.
Abu Nowshed Chy +2 more
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Sentiment Analysis Using Naive Bayes Classifier
Abstract— Sentiment analysis, a subfield of natural language processing, plays a vital role in understanding public opinion and sentiment towards products, services, or events. In this study, we explore the field of sentiment analysis with a special emphasis on the use of machine learning techniques to classify the sentiments set in textual data.Ann Mary Ajith, Gloriya Mathew
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Optimizing MapReduce Partitioner Using Naive Bayes Classifier
2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), 2017Data locality and data skew on the reduce side are two essential issues in MapReduce. Improving data locality can decrease network traffic by moving reduce tasks to the nodes where the input data of reduce tasks is located. Data skew will lead to load imbalance among reducer nodes.
Lei Chen +6 more
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The naive Bayes classifier for functional data
Statistics & Probability Letters, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yi-Chen Zhang, Lyudmila Sakhanenko
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Text filtering by boosting naive Bayes classifiers
Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, 2000Several machine learning algorithms have recently been used for text categorization and filtering. In particular, boosting methods such as AdaBoost have shown good performance applied to real text data. However, most of existing boosting algorithms are based on classifiers that use binary-valued features.
Yu-Hwan Kim +2 more
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Sms Spam Classifier by Naive Bayes Classifier
SSRN Electronic Journal, 2023Pankaj Gupta +4 more
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