Naïve垃圾邮件检测的贝叶斯分类器模型

Q1 Decision Sciences

Annals of Data Science Pub Date : 2023-06-09 DOI:10.1007/s40745-023-00479-z

Shrawan Kumar, Kavita Gupta, Manya Gupta

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引用次数: 0

摘要

本文将机器学习算法 Naive Bayes 分类器应用于 Kaggle 垃圾邮件数据集，将收件箱中的邮件分为垃圾邮件和火腿肠邮件。数据集由两个主要属性组成：类型和文本。目标变量 "类型 "包含两个因子：垃圾邮件和火腿邮件。文本变量包含将被分类为垃圾邮件或火腿肠邮件的文本信息。结果是通过使用两种不同的拉普拉斯值得出的。决策者可以根据两种不同的拉普拉斯值来选择火腿和垃圾邮件的误差容限。计算软件 R 用于数据分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Naïve Bayes Classifier Model for Detecting Spam Mails

In this paper, the machine learning algorithm Naive Bayes Classifier is applied to the Kaggle spam mails dataset to classify the emails in our inbox as spam or ham. The dataset is made up of two main attributes: type and text. The target variable "Type" has two factors: ham and spam. The text variable contains the text messages that will be classified as spam or ham. The results are obtained by employing two different Laplace values. It is up to the decision maker to select error tolerance in ham and spam messages derived from two different Laplace values. Computing software R is used for data analysis.

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来源期刊

Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

6.50

自引率

0.00%

发文量

期刊介绍： Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.