Novel Email Spam Classification using Integrated Particle Swarm Optimization and J48

Kaur, Harpreet; Sharma, Ajay

doi:10.5120/ijca2016911466

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…In this part, three related works were investigated. The first one was based on PSO and the decision tree J48 [8], the second tackled MLP neural network and biogeography-based optimization [9], while the last work proposed SVMbased PSO and MLP [10]. The third category includes the comparison with DL related works.…”

Section: Comparative Analysismentioning

confidence: 99%

“…The highest achieved accuracy is 94.2% obtained by using the random forest classifier [7]. To enhance the performance of the machine learning (ML) algorithms, many studies combined them with bio-inspired algorithms, or artificial neural networks [8]- [10]. Particle swarm optimization (PSO) is among the most popular algorithms that has been used in spam email detection.…”

Section: Introductionmentioning

confidence: 99%

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Improving spam email detection using deep recurrent neural network

Marie-Sainte

Ghouzali

Saba

et al. 2022

IJEECS

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<span>Nowadays the entire world depends on emails as a communication tool. Spammers try to exploit various vulnerabilities to attack users with spam emails. While it is difficult to prevent spam email attacks, many research studies have been developed in the last decade in an attempt to detect spam emails. These studies were conducted using machine learning techniques and various types of neural networks. However, with all their attempts the highest accuracy acquired was 94.2% by random forest classifier. Deep learning techniques have demonstrated higher accuracy performance compared to the traditional machine learning algorithms. In this paper, deep recurrent neural network was used to determine whether an email is a spam email. After investigating different configurations for this method, the best setting that generated the highest accuracy was based on using Tanh as the activation function with the dropout rate equals to 0.1 and the number of epochs achieving 100. The proposed approach attained a high accuracy of 99.7% which surpassed the best accuracy (98.7%) obtained by the hybrid gated recurrent unit recurrent neural network approach.</span>

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Section: Comparative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving spam email detection using deep recurrent neural network

Marie-Sainte

Ghouzali

Saba

et al. 2022

IJEECS

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“…J48 menjadi salah satu pengembangan dari algoritma decision tree yang memiliki kinerja yang baik. Algritma ini membuat pohon keputusan yang tergantung pada nilai-nilai atribut dari data pelatihan yang tersedia terhadap klasifikasi item baru [18]. Algoritima akan menganalisis semua item pelatihan dengan mengenali berbagai atribut untuk membedakan beragam contoh dengan lebih jelas.…”

Section: Decision Treeunclassified

Implementasi Teknik Bagging untuk Peningkatan Kinerja J48 dan Logistic Regression dalam Prediksi Minat Pembelian Online

Rahmawati

Agustina

2020

J-TIT

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Abstract—The rapid growth of online shopping sites makes business in the virtual world very promising. Purchasing intentions is one of the keys to success in an online store. There are several data mining methods for making predictions on online purchase intentions datasets. Data can represent the characteristics or habits of each user who has visited a site whether it ends with a transaction or not. Some popular algorithms with good performance in data mining include J48 and Logistic Regression. However, in data sometimes there is a problem of class imbalance, so the ensemble technique needs to be applied. One technique that can be applied is bagging. This research examines data using bagging techniques to improve the performance of the J48 algorithm and Logistic Regression. The results of improving the performance of data mining algorithms with these techniques have an accuracy value of 89.68% for the J48 algorithm and 88.50% for the Logistic Regression algorithm. This figure shows an increase when compared with initial testing without using ensemble techniques. Increases were also experienced in Recall, F-Measure, and AUC values. Keywords—purchasing intentions; J48; Logistic Regression; Bagging; Abstrak— Pesatnya situs pembelanjaan online menjadikan bisnis di dunia virtual sangat menjanjikan. Minat pembelian menjadi salah satu kunci kesuksesan pada sebuah toko online. Terdapat beberapa metode data mining untuk melakukan prediksi pada dataset minat pembelian online. Data dapat mewakili karakteristik atau kebiasaan dari setiap user yang telah mengunjungi suatu situs baik berakhir dengan melakukan transaksi ataupun tidak. Beberapa algoritma yang populer dengan kinerja yang baik dalam data mining diantaranya J48 dan Logistic Regreession. Namun, dalam sebuah data terkadang terdapat masalah ketidakseimbangan kelas, sehingga perlu diterapkan teknik ensemble. Salah satu teknik yang dapat diterapkan adalah teknik bagging. Penelitian kali ini mengujikan data dengan teknik bagging untuk meningkatkan kinerja algoritma J48 dan Logistic Regression. Hasil dari peningkatan kinerja algoritma data mining dengan teknik tersebut memiliki nilai akurasi 89.68% untuk algoritma J48 dan 88.50% untuk algoritma Logistic Regression. Angka tersebut menunjukan adanya peningkatan jika dibandingkan dengan pengujian awal tanpa menggunakan teknik ensemble. Peningkatan juga dialami pada nilai Recall, F-Measure, dan AUC. Keywords—Minat Pembelian, J48, Logistic Regression, Bagging

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Improving Spam Email Filtering Systems Using Data Mining Techniques

Awad

Rafiq

2020

Implementing Computational Intelligence Techniques for Security Systems Design

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Email is the most popular choice of communication due to its low-cost and easy accessibility, which makes email spam a major issue. Emails can be incorrectly marked by a spam filter and legitimate emails can get lost in the spam folder or the spam emails can deluge the users' inboxes. Therefore, various methods based on statistics and machine learning have been developed to classify emails accurately. In this chapter, the existing spam filtering methods were studied comprehensively, and a spam email classifier based on the genetic algorithm was proposed. The proposed algorithm was successful in achieving high accuracy by reducing the rate of false positives, but at the same time, it also maintained an acceptable rate of false negatives. The proposed algorithm was tested on 2000 emails from the two popular spam datasets, Enron and LingSpam, and the accuracy was found to be nearly 90%. The results showed that the genetic algorithm is an effective method for spam classification and with further enhancements that will provide a more robust spam filter.

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Novel Email Spam Classification using Integrated Particle Swarm Optimization and J48

Cited by 3 publications

References 14 publications

Improving spam email detection using deep recurrent neural network

Improving spam email detection using deep recurrent neural network

Implementasi Teknik Bagging untuk Peningkatan Kinerja J48 dan Logistic Regression dalam Prediksi Minat Pembelian Online

Improving Spam Email Filtering Systems Using Data Mining Techniques

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