Enhanced genetic algorithm for spam detection in email

Salehi, Saber; Selamat, Ali; Bostanian, Mohammad

doi:10.1109/icsess.2011.5982390

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…Metaheuristic algorithms have recently been used to classify spam. Salehi et al 28 introduced a new method for the revealing of spam e-mails using a genetic algorithm (GA). Pandey and Rajpoot 29 deployed a cuckoo search (CS) algorithm with MA for the detection of spam reviews.…”

Section: Literature Reviewmentioning

confidence: 99%

Memetic Spider Monkey Optimization for Spam Review Detection Problem

2023

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Spider monkey optimization (SMO) algorithm imitates the spider monkey's fission-fusion social behavior. It is evident through literature that the SMO is a competitive swarm-based algorithm that is used to solve difficult real-life problems. The SMO's search process is a little bit biased by the random component that drives it with high explorative searching steps. A hybridized SMO with a memetic search to improve the local search ability of SMO is proposed here. The newly developed strategy is titled Memetic SMO (MeSMO). Further, the proposed MeSMO-based clustering approach is applied to solve a big data problem, namely, the spam review detection problem. A customer usually makes decisions to purchase something or make an image of someone based on online reviews. Therefore, there is a good chance that the individuals or companies may write spam reviews to upgrade or degrade the stature or value of a trader/product/company. Therefore, an efficient spam detection algorithm, MeSMO, is proposed and tested over four complex spam datasets. The reported results of MeSMO are compared with the outcomes obtained from the six state-of-art strategies. A comparative analysis of the results proved that MeSMO is a good technique to solve the spam review detection problem and improved precision by 3.68%.

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Section: Literature Reviewmentioning

confidence: 99%

Memetic Spider Monkey Optimization for Spam Review Detection Problem

2023

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“…The process of producing new individuals is done by two significant Genetic Operators i.e., "Crossover" and "Mutation". Crossover operates by random selection of a point in two parent gene structure and develops two new individuals by exchanging the remaining part of parents [11]. In Knowledge discovery process, Various machine learning algorithms and mechanisms are used such as Classification, Clustering, Regression, Association Rules, Decision Trees, Genetic Algorithm, Nearest Neighbor method etc., are used.…”

Section: Inductive Logic Programmingmentioning

confidence: 99%

A Framework for Implementing Machine Learning algorithms using Data sets

Chandra¹,

Ranjith²,

Shanthisri³

et al. 2019

IJITEE

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The rapid development of cloud computing, big data, machine learning and datamining made information technology and human society to enter new era of technology. Statistical and mathematical analysis on data given a new way of research on prediction and estimation using samples and data sets. Data mining is a mechanism that explores and analyzes many dis-organized or dis-ordered data to obtain potentially useful information and model it based on different algorithms. Machine learning is an iterative process rather than a linear process that requires each step to be revisited as more is learned about the problem. We discussed different machine learning algorithms that can manipulate data and analyses datasets based on best cases for accurate results. Design and Implementation of a framework that is associated with different machine learning algorithms. This paper expounds the definition, model, development stage, classification and commercial application of machine learning, and emphasizes the role of machine learning in data mining by deploying the framework. Therefore, this paper summarizes and analyzes machine learning technology, and discusses the use of machine learning algorithms in data mining. Finally, the mathematical analysis along with results and graphical analysis is given

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“…An Enhanced Genetic Algorithm (EGA) model was used in [11], which is an enhanced version of Genetic Algorithm (GA) through combination with Simulated annealing (SA), is proposed for spotting spam emails. Assessment is carried out on Spam Corpus with 54 characteristics.…”

Section: Wwwetasrcom Gashti Et Al: Detection Of Spam Email By Combining Harmony Search Algorithm and Decision Treementioning

confidence: 99%

Detection of Spam Email by Combining Harmony Search Algorithm and Decision Tree

Gashti

2017

Eng. Technol. Appl. Sci. Res.

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Spam emails is probable the main problem faced by most e-mail users. There are many features in spam email detection and some of these features have little effect on detection and cause skew detection and classification of spam email. Thus, Feature Selection (FS) is one of the key topics in spam email detection systems. With choosing the important and effective features in classification, its performance can be optimized. Selector features has the task of finding a subset of features to improve the accuracy of its predictions. In this paper, a hybrid of Harmony Search Algorithm (HSA) and decision tree is used for selecting the best features and classification. The obtained results on Spam-base dataset show that the rate of recognition accuracy in the proposed model is 95.25% which is high in comparison with models such as SVM, NB, J48 and MLP. Also, the accuracy of the proposed model on the datasets of Ling-spam and PU1 is high in comparison with models such as NB, SVM and LR.

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Enhanced genetic algorithm for spam detection in email

Cited by 14 publications

References 3 publications

Memetic Spider Monkey Optimization for Spam Review Detection Problem

Memetic Spider Monkey Optimization for Spam Review Detection Problem

A Framework for Implementing Machine Learning algorithms using Data sets

Detection of Spam Email by Combining Harmony Search Algorithm and Decision Tree

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