An Efficient feature selection algorithm for the spam email classification

Saleh, Hadeel M.

doi:10.21533/pen.v9i3.2202

Cited by 6 publications

(3 citation statements)

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“…Using the random forest as a classifier and the methods of least redundancy and maximum relevance for feature selection, Sri and Karthika [14] achieved an accuracy of 86%. To reduce feature dimensionality, Saleh [15] suggested a hybrid method employing Chaotic Particle Swarm Optimization and Artificial Bees Colony, which had a 90.81% accuracy. Using the Spambase Dataset, Soleimanian, and Mousavi [16] examined how well different network models performed and discovered that just 10 of the 57 features could obtain a classification accuracy of 91.7%.…”

Section: Background Workmentioning

confidence: 99%

A Lightweight Machine Learning-Based Email Spam Detection Model Using Word Frequency Pattern

Bouke,

Abdullah,

Abdullah

et al. 2023

J. Info. Tech. Comp.

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This Spam emails have become a severe challenge that irritates and consumes recipients' time. On the one hand, existing spam detection techniques have low detection rates and cannot tolerate high-dimensional data. Moreover, due to the machine learning algorithm's effectiveness in identifying mail as solicited or unsolicited, their approaches have become common in spam detection systems. This paper proposes a lightweight machine learning-based spam detection model based on Random Forest (RF) algorithm. According to the empirical results, the proposed model achieved a 97% accuracy on the spambase dataset. Furthermore, the performance of the proposed model was evaluated using standard classification metrics such as Fscore, Recall, Precision, and Accuracy. The comparison of Our model with state-of-the-art works investigated in this paper showed the model performs better, with an improvement of 6% for all metrics.

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Section: Background Workmentioning

confidence: 99%

A Lightweight Machine Learning-Based Email Spam Detection Model Using Word Frequency Pattern

Bouke,

Abdullah,

Abdullah

et al. 2023

J. Info. Tech. Comp.

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“…It has traditionally been used to solve many issues [7]. It is a method of global optimization in machine learning [8]. However, feature selection has been a hot topic for decades, and improves the efficiency of data mining and analysis [9].…”

Section: Introductionmentioning

confidence: 99%

Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

ElDahshan¹,

AlHabshy²,

Mohammed³

2023

Computers, Materials &Amp; Continua

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This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods. To reduce the volume of big data and minimize model training time (Tt) while maintaining data quality. We contributed to meeting the challenges of big data visualization using the embedded method based "Select from model (SFM)" method by using "Random forest Importance algorithm (RFI)" and comparing it with the filter method by using "Select percentile (SP)" method based chi square "Chi2" tool for selecting the most important features, which are then fed into a classification process using the logistic regression (LR) algorithm and the k-nearest neighbor (KNN) algorithm. Thus, the classification accuracy (AC) performance of LR is also compared to the KNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are applied. Consequently, the study concluded that the feature selection methods have a significant impact on the analysis and visualization of the data after removing the repetitive data and the data that do not affect the goal. After making several comparisons, the study suggests (SFMLR) using SFM based on RFI algorithm for feature selection, with LR algorithm for data classify. The proposal proved its efficacy by comparing its results with recent literature.

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“…Saleh [14] proposes the integration of the Chaotic particle swarm optimization (PSO) method with Artificial Bees Colony (ABC) to minimize the dimensionality of features in a bid to enhance spam emails classifier accuracy. The structures for every particle in this study have been indicated in a binary format, that they are transmitted into binary utilizing a sigmoid function.…”

Section: Introductionmentioning

confidence: 99%

Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

Alshahrani¹,

Tarmissi²,

Yafoz³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns. Emails stay in the leading positions for business as well as personal use. This popularity grabs the interest of individuals with malevolent intentions-phishing and spam email assaults. Email filtering mechanisms were developed incessantly to follow unwanted, malicious content advancement to protect the end-users. But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced. Thus, this study provides a solution related to email message body text automatic classification into phishing and email spam. This paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network (IFFO-SRRNN) based on Applied Linguistics for Email Classification. The presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam emails. At the preliminary level, the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further computation. Next, the SRRNN method can be useful in recognizing and classifying spam emails. As hyperparameters of the SRRNN model need to be effectually tuned, the IFFO algorithm can be utilized as a hyperparameter optimizer. To investigate the effectual email classification results of the IFFO-SRDL technique, a series of simulations were taken placed on public datasets, and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

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An Efficient feature selection algorithm for the spam email classification

Cited by 6 publications

References 0 publications

A Lightweight Machine Learning-Based Email Spam Detection Model Using Word Frequency Pattern

A Lightweight Machine Learning-Based Email Spam Detection Model Using Word Frequency Pattern

Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

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