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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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