Machine Learning methods for E-mail Classification

Awad, Wael A.; Elseuofi, S. M.

doi:10.5120/1974-2646

Cited by 38 publications

(8 citation statements)

References 11 publications

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“…The report by Awad et al [3] is more of an educational paper than a research paper. The author reviews six most popular classification methods (Bayesian classification, ANNs, SVMs, k-NN, Rough sets, and Artificial immune system) to perform a spam email classification task.…”

Section: Related Workmentioning

confidence: 99%

How Much Is My Car Worth? A Methodology for Predicting Used Cars’ Prices Using Random Forest

Pal

Arora

Kohli

et al. 2018

Advances in Intelligent Systems and Computing

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Cars are being sold more than ever. Developing countries adopt the lease culture instead of buying a new car due to affordability. Therefore, the rise of used cars sales is exponentially increasing. Car sellers sometimes take advantage of this scenario by listing unrealistic prices owing to the demand. Therefore, arises a need for a model that can assign a price for a vehicle by evaluating its features taking the prices of other cars into consideration. In this paper, we use supervised learning method namely Random Forest to predict the prices of used cars. The model has been chosen after careful exploratory data analysis to determine the impact of each feature on price. A Random Forest with 500 Decision Trees were created to train the data. From experimental results, the training accuracy was found out to be 95.82%, and the testing accuracy was 83.63%. The model can predict the price of cars accurately by choosing the most correlated features.

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Section: Related Workmentioning

confidence: 99%

How Much Is My Car Worth? A Methodology for Predicting Used Cars’ Prices Using Random Forest

Pal

Arora

Kohli

et al. 2018

Advances in Intelligent Systems and Computing

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“…The attribute which has the greatest information gain will be chosen. To calculate two types of entropy and information gain, formulas are defined in Eqs ( 6) and (7).…”

Section: Classification Techniquesmentioning

confidence: 99%

“…Overfitting occurs when the complex model is made for a simple dataset. Mostly spam email affect the user in the form of time consumption while reading spam email, bandwidth and in form of space that is required for the storage of spam email [7]. Users spend a lot of time reading spam emails which are useless for them.…”

Section: Introductionmentioning

confidence: 99%

Email classification analysis using machine learning techniques

Iqbal

Khan

2022

ACI

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PurposeIn this digital era, email is the most pervasive form of communication between people. Many users become a victim of spam emails and their data have been exposed.Design/methodology/approachResearchers contribute to solving this problem by a focus on advanced machine learning algorithms and improved models for detecting spam emails but there is still a gap in features. To achieve good results, features also play an important role. To evaluate the performance of applied classifiers, 10-fold cross-validation is used.FindingsThe results approve that the spam emails are correctly classified with the accuracy of 98.00% for the Support Vector Machine and 98.06% for the Artificial Neural Network as compared to other applied machine learning classifiers.Originality/valueIn this paper, Point-Biserial correlation is applied to each feature concerning the class label of the University of California Irvine (UCI) spambase email dataset to select the best features. Extensive experiments are conducted on selected features by training the different classifiers.

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“…Naive Bayes algorithm is one of the machine learning methods that is used in text classification. It is a statistical inference based on probability, and is used to determine previously created classes [9]. NB uses a discriminant function to compute the conditional probabilities of P(Ci|X).…”

Section: Naive Bayes (Nb)mentioning

confidence: 99%

Destek Vektör Makineleri Kullanılarak Spam SMS Tespiti

Tekerek

2019

Politeknik Dergisi

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Short Message Service (SMS) is the most important communication tool in recent decades. With the increased popularity of mobile devices, the usage rate of SMS will increase more and more in years. SMS is a practical method used to reach individuals directly. But this practical and easy method can cause SMS to be misused. The advertising or promotional SMS of the companies are an examples of this misuse. In this study, a spam SMS detection technique is proposed using Data Mining (DM) methods. In the proposed study, data mining algorithms such as Naive Bayes (NB), K-Nearest Neighborhood (KNN), Support Vector Machine (SVM), Random Forest (RF) and Random Tree (RT) is selected. SMSSpamCollection dataset, which is contain 747 spam SMS and 4827 ham SMS, is used. 10 fold cross-validation technique is used to evaluate prediction of Spam SMS in the dataset. Therefore, proposed study achieved 98.33 % success rate and 0,087 false positive rate for SVM algorithm..

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Machine Learning methods for E-mail Classification

Cited by 38 publications

References 11 publications

How Much Is My Car Worth? A Methodology for Predicting Used Cars’ Prices Using Random Forest

How Much Is My Car Worth? A Methodology for Predicting Used Cars’ Prices Using Random Forest

Email classification analysis using machine learning techniques

Destek Vektör Makineleri Kullanılarak Spam SMS Tespiti

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