Spam Detection Using Machine Learning in R

Kumari, K. R. Vidya; Kavitha, C. R.

doi:10.1007/978-981-10-8681-6_7

Cited by 8 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This Random Forest classifier is tested with test dataset and gives accurate results than other classifiers for this spam dataset. Paper [12] classifies the tweets into spam and non-spam using machine learning techniques. The results show that Naïve Bayes gives better results than Support Vector Machine (SVM).…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Various Machine Learning and Deep Learning Algorithms used for Classification of Spam and Non-Spam Emails

Khanday¹

2019

IJRASET

View full text Add to dashboard Cite

An email client receives emails from different websites, portals and domains, which can be an advertisement. Receiving a bulk amount of emails can cause serious damages like suspension of a particular email id.Mostly an email client gets exposed to the number of malicious receipts by registering an email account to a web portal, which in turn sends a bulk amount of emails. The email client wants to be decisive about differentiating the useful emails and spam emails. One of the solutions to escape from spam emails is to develop a decision based system which can classify the spam and non-spam emails. This survey gives an overview about different machine learning and deep learning algorithms to classify the spam and non-spam emails by accessing the received emails of an email client. The machine learning approaches and mechanisms like support vector machine, naive Bayesian classifier, artificial neural networks and logistic regression can be of important help to determine spam emails. These approaches use decision trees to run tests on a given sets of data (emails). After classifying a spam email source a user can navigate, block and report the source of the spam email generator. Most of the times the spam emails are generated by the autonomous sources which are called spam-bots.

show abstract

Section: Literature Surveymentioning

confidence: 99%

A Survey on Various Machine Learning and Deep Learning Algorithms used for Classification of Spam and Non-Spam Emails

Khanday¹

2019

IJRASET

View full text Add to dashboard Cite

show abstract

Spam Classification Based on Supervised Learning Using Grasshopper Optimization Algorithm and Artificial Neural Network

Ghaleb

Mohamad

Abdullah

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Multi-source fast transfer learning algorithm based on support vector machine

Gao

2021

Appl Intell

View full text Add to dashboard Cite

Knowledge in the source domain can be used in transfer learning to help train and classification tasks within the target domain with fewer available data sets. Therefore, given the situation where the target domain contains only a small number of available unlabeled data sets and multi-source domains contain a large number of labeled data sets, a new Multi-source Fast Transfer Learning algorithm based on support vector machine(MultiFTLSVM) is proposed in this paper. Given the idea of multi-source transfer learning, more source domain knowledge is taken to train the target domain learning task to improve classification effect. At the same time, the representative data set of the source domain is taken to speed up the algorithm training process to improve the efficiency of the algorithm. Experimental results on several real data sets show the effectiveness of MultiFTLSVM, and it also has certain advantages compared with the benchmark algorithm.

show abstract

Spam Detection Using Machine Learning in R

Cited by 8 publications

References 6 publications

A Survey on Various Machine Learning and Deep Learning Algorithms used for Classification of Spam and Non-Spam Emails

A Survey on Various Machine Learning and Deep Learning Algorithms used for Classification of Spam and Non-Spam Emails

Spam Classification Based on Supervised Learning Using Grasshopper Optimization Algorithm and Artificial Neural Network

Multi-source fast transfer learning algorithm based on support vector machine

Contact Info

Product

Resources

About