A New Hybrid Rough Set and Soft Set Parameter Reduction Method for Spam E-Mail Classification Task

Mohamad, Mohd Saberi; Selamat, Ali

doi:10.1007/978-3-319-42706-5_2

Cited by 3 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first set of optimised feature sets was selected as the most optimised dataset. Notably, this concept has been implemented previously and proven effective in previous works by [66][67][68].…”

mentioning

confidence: 99%

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

et al. 2021

Self Cite

View full text Add to dashboard Cite

This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based feature selection (CFS), best first search (BFS), and dominance-based rough set approach (DRSA) methods. This study aims to enhance the classifier’s performance in decision analysis by eliminating uncorrelated and inconsistent data values. The proposed method, named CFS-DRSA, comprises several phases executed in sequence, with the main phases incorporating two crucial feature extraction tasks. Data reduction is first, which implements a CFS method with a BFS algorithm. Secondly, a data selection process applies a DRSA to generate the optimized dataset. Therefore, this study aims to solve the computational time complexity and increase the classification accuracy. Several datasets with various characteristics and volumes were used in the experimental process to evaluate the proposed method’s credibility. The method’s performance was validated using standard evaluation measures and benchmarked with other established methods such as deep learning (DL). Overall, the proposed work proved that it could assist the classifier in returning a significant result, with an accuracy rate of 82.1% for the neural network (NN) classifier, compared to the support vector machine (SVM), which returned 66.5% and 49.96% for DL. The one-way analysis of variance (ANOVA) statistical result indicates that the proposed method is an alternative extraction tool for those with difficulties acquiring expensive big data analysis tools and those who are new to the data analysis field.

show abstract

mentioning

confidence: 99%

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

Analysis on Hybrid Dominance-Based Rough Set Parameterization Using Private Financial Initiative Unitary Charges Data

Mohamad

Selamat

2018

Intelligent Information and Database Systems

View full text Add to dashboard Cite

Email Filtering Using Hybrid Feature Selection Model

Mohammad¹,

Smadi²,

Alwada’n³

2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Undoubtedly, spam is a serious problem, and the number of spam emails is increased rapidly. Besides, the massive number of spam emails prompts the need for spam detection techniques. Several methods and algorithms are used for spam filtering. Also, some emergent spam detection techniques use machine learning methods and feature extraction. Some methods and algorithms have been introduced for spam detecting and filtering. This research proposes two models for spam detection and feature selection. The first model is evaluated with the email spam classification dataset, which is based on reducing the number of keywords to its minimum. The results of this model are promising and highly acceptable. The second proposed model is based on creating features for spam detection as a first stage. Then, the number of features is reduced using three well-known metaheuristic algorithms at the second stage. The algorithms used in the second model are Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), and these three algorithms are adapted to fit the proposed model. Also, the authors give it the names AABC, AACO, and APSO, respectively. The dataset used for the evaluation of this model is Enron. Finally, well-known criteria are used for the evaluation purposes of this model, such as true positive, false positive, false negative, precision, recall, and F-Measure. The outcomes of the second proposed model are highly significant compared to the first one.

show abstract

A New Hybrid Rough Set and Soft Set Parameter Reduction Method for Spam E-Mail Classification Task

Cited by 3 publications

References 21 publications

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

Analysis on Hybrid Dominance-Based Rough Set Parameterization Using Private Financial Initiative Unitary Charges Data

Email Filtering Using Hybrid Feature Selection Model

Contact Info

Product

Resources

About