Application of Naive Bayesian Algorithms in E-mail Classification

In the context of the rapid development of today's society, high-tech information technology such as machine learning has played a very important role in promoting social, economic and technological development in all countries of the world. Bayesian classification algorithm is an important alg...

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Vydáno v:Chinese Automation Congress (Online) s. 3933 - 3938
Hlavní autoři: Chen, Rui, Zhang, Cai-xia, Guo, Jing, Wang, Xiang-dong
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2019
Témata:
Bayes methods
data mining
Data models
Dictionaries
email classification
Hidden Markov models
Machine learning
Monitoring
naive Bayesian algorithm
Postal services
Smart cities
text classification
ISSN:2688-0938
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Popis
Shrnutí:In the context of the rapid development of today's society, high-tech information technology such as machine learning has played a very important role in promoting social, economic and technological development in all countries of the world. Bayesian classification algorithm is an important algorithm in the field of machine learning and data mining. Naive Bayesian algorithm is a basic and simple classification algorithm in Bayesian classification algorithm. The naive Bayesian algorithm has the advantages of high stability, simplicity, high efficiency and strong theoretical foundation. The classification quality of the naive Bayesian algorithm much depends on the choice of construction methods and the nature and quantity of the classification data. In fact, many problems in our life can be attributed to classification problems, such as risk prediction, medical diagnosis, e-mail classification, fraud detection, etc. [1]. Here, I will mainly explain the principle of naive Bayesian algorithm, classification process, the evaluation of classification results, and apply this algorithm to e-mail classification tasks. At the same time, I list some related examples. Finally, I will summarize the naive Bayesian algorithm.
ISSN:2688-0938
DOI:10.1109/CAC48633.2019.8997251