Machine intelligence-based algorithms for spam filtering on document labeling

Gaurav, Devottam; Tiwari, Sanju; Goyal, Ayush; Gandhi, Niketa; Abraham, Ajith

doi:10.1007/s00500-019-04473-7

Cited by 45 publications

(16 citation statements)

References 14 publications

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“…Guarav et al [16] examined the efficiency of NB, DT, and RF algorithms used in the classification process. The experiments were carried out on three different types of datasets: Lingspam, Enron and PU.…”

Section: Related Workmentioning

confidence: 99%

A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

2021

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The paper elaborates on how text analysis influences classification—a key part of the spam-filtering process. The authors propose a multistage meta-algorithm for checking classifier performance. As a result, the algorithm allows for the fast selection of the best-performing classifiers as well as for the analysis of higher-dimensionality data. The last aspect is especially important when analyzing large datasets. The approach of cross-validation between different datasets for supervised learning is applied in the meta-algorithm. Three machine-learning methods allowing a user to classify e-mails as desirable (ham) or potentially harmful (spam) messages were compared in the paper to illustrate the operation of the meta-algorithm. The used methods are simple, but as the results showed, they are powerful enough. We use the following classifiers: k-nearest neighbours (k-NNs), support vector machines (SVM), and the naïve Bayes classifier (NB). The conducted research gave us the conclusion that multinomial naïve Bayes classifier can be an excellent weapon in the fight against the constantly increasing amount of spam messages. It was also confirmed that the proposed solution gives very accurate results.

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Section: Related Workmentioning

confidence: 99%

A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

2021

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“…In 2020, Gaurav et al [16] proposed spam mail detection (SPMD) method based on the document labeling concept, which sorts the new messages into two categories: Ham and Spam. Experimental results illustrated that Random Forest produced the highest accuracy of 92.97% among the three classification models: Naive Bayes, Decision Tree and Random Forest.…”

Section: Related Workmentioning

confidence: 99%

Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text

Shaaban,

Hassan,

Guirguis

2021

Preprint

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The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people's fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.

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“…The spam base dataset is an acquisition from email spam message and we can achieve the corpus benchmark from it. 7 In Reference 52, the total number of acquired messages in the dataset was 4601 and 39% of them (ie, 1813 messages) were marked to be spam messages and the remaining 61% (ie, 2788 messages) were identified as nonspam. The nonspam message was acquired from a single mailbox and gave by Forman.…”

Section: Spam Base Dataset Analysismentioning

confidence: 99%

“…FS techniques are categorized as filters and wrappers. [4][5][6] Given the following factors, FS is fundamental processing in real-world problems 7 : (i) the huge variety of noise, (ii) false or counterfeit information, and (iii) redundant and irrelevant features in the original feature set. Therefore, FS has become an important and active research topic in a variety of fields such as data mining, pattern recognition, text categorization, and image mining.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

Mohammadzadeh

Gharehchopogh

2020

Computational Intelligence

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Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition-based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar 176

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Machine intelligence-based algorithms for spam filtering on document labeling

Cited by 45 publications

References 14 publications

A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text

A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

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