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Pseudo NLP Joint spam Classification Technique for Big Data Cluster

Computers, Materials & Continua 2022年第4期71卷 517-535页

作者： WooHyun Park Nawab Muhammad Faseeh Qureshi Dong Ryeol Shin Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon16419Korea Department of Computer Education Sungkyunkwan UniversitySeoul03063Korea

spam mail classification considered complex and error-prone task in the distributed computing *** are various available spam mail classification approaches such as the naive Bayesian classifier,logistic regression and support vector machine and decision tree,recursive neural network,and long short-term memory ***,they do not consider the document when analyzing spam mail *** approaches use the bagof-words method,which analyzes a large amount of text data and classifies features with the help of term frequency-inverse document *** there are many words in a document,these approaches consume a massive amount of resources and become infeasible when performing classification on multiple associated mail documents ***,spam mail is not classified fully,and these approaches remain with ***,we propose a term frequency topic inverse document frequency model that considers the meaning of text data in a larger semantic unit by applying weights based on the document’s ***,the proposed approach reduces the scarcity problem through a frequency topic-inverse document frequency in singular value decomposition *** proposed approach also reduces the dimensionality,which ultimately increases the strength of document *** evaluations show that the proposed approach classifies spam mail documents with higher accuracy using individual document-independent processing *** evaluations show that the proposed approach performs better than the logistic regression model in the distributed computing environment,with higher document word frequencies of 97.05%,99.17%and 96.59%.

关键词： NLP big data machine learning TFT-IDF spam mail

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Improving Accuracy of spam Detection

引用

Computer Aided Drafting,Design and Manufacturing 2008年第2期18卷 103-108页

作者： HANI M. O. Iwidat School of Computer Science and Engineering Beijing University of Aeronautics and Astronautics

spam, the electronic equivalent of junk mail, affects over 600 million users worldwide. The massive increase of spam is posing a very serious threat to email which has become an important means of communication. Not only does it annoy users, but it also consumes much of the bandwidth of the Internet. Even as anti-spam solutions change to limit the amount of spam sent to users, the senders adapt to make sure their messages are seen, while spam reorganization has different properties comparing with normal text reorganization. Presented are three different classifiers are combined with detailed analysis on various training data set of the given spam database. These classifiers are combined into a mixture of experts (MOE) system which yields overall better performance than any of the individual contributors. The instructions for further improvements on classifiers as well as its requirement on spam databases are also given.

关键词： spam mail classification KNN mixture system

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