Feature Extraction and Classification of Spam Emails

Hassan, Muhammad Ali; Mtetwa, Nhamoinesu

doi:10.1109/iscmi.2018.8703222

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…• Feature Engineering (FE)-Feature engineering techniques are used in text processing to extract relevant features from data. When applying a well chosen feature engineering technique, it has been shown to provide a significant increase in the accuracy of the deep neural networks that use the produced feature representation, both in the case of sentiment analysis [14] as well as spam detection [15,16].…”

Section: Deep Neural Network Pipelinementioning

confidence: 99%

Automated Design of the Deep Neural Network Pipeline

Gerber

Pillay

2022

Applied Sciences

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Deep neural networks have proven to be effective in various domains, especially in natural language processing and image processing. However, one of the challenges associated with using deep neural networks includes the long design time and expertise needed to apply these neural networks to a particular domain. The research presented in this paper investigates the automation of the design of the deep neural network pipeline to overcome this challenge. The deep learning pipeline includes identifying the preprocessing needed, the feature engineering technique, the neural network to use and the parameters for the neural network. A selection pertubative hyper-heuristic (SPHH) is used to automate the design pipeline. The study also examines the reusability of the generated pipeline. The effectiveness of transfer learning on the generated designs is also investigated. The proposed approach is evaluated for text processing—namely, sentiment analysis and spam detection—and image processing—namely, maize disease detection and oral lesion detection. The study revealed that the automated design of the deep neural network pipeline produces just as good, and in some cases better, performance compared to the manual design, with the automated design requiring less design time than the manual design. In the majority of instances, the design was not reusable; however, transfer learning achieved positive transfer of designs, with the performance being just as good or better than when transfer learning was not used.

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Section: Deep Neural Network Pipelinementioning

confidence: 99%

Automated Design of the Deep Neural Network Pipeline

Gerber

Pillay

2022

Applied Sciences

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“…In this study, the Term Frequency Inverse Document Frequency (TF-IDF) method was employed as a feature extraction method. It is a combination of TF and IDF [28]. According to Kadhim [29], this helps to capture features that are more important within the body of an email.…”

Section: Feature Extractionmentioning

confidence: 99%

Low Time Complexity Model for Email Spam Detection using Logistic Regression

Mrisho¹,

Ndibwile²,

Sam³

2021

IJACSA

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Spam emails have recently become a concern on the Internet. Machine learning techniques such as Neural Networks, Naïve Bayes, and Decision Trees have frequently been used to combat these spam emails. Despite their efficiency, time complexity in high-dimensional datasets remains a significant challenge. Due to a large number of features in high-dimensional datasets, the intricacy of this problem grows exponentially. The existing approaches suffer from a computational burden when thousands of features are used (high-time complexity). To reduce time complexity and improve accuracy in high-dimensional datasets, extra steps of feature selection and parameter tuning are necessary. This work recommends the use of a hybrid logistic regression model with a feature selection approach and parameter tuning that could effectively handle a big dimensional dataset. The model employs the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction method to mitigate the drawbacks of Term Frequency (TF) to obtain an equal feature weight. Using publicly available datasets (Enron and Lingspam), we compared the model's performance to that of other contemporary models. The proposed model achieved a low level of time complexity while maintaining a high level of spam detection rate of 99.1%.

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