Training Logistic Regression Model by Hybridized Multi-verse Optimizer for Spam Email Classification

Živković, Miodrag; Petrovic, A. Martin; Bačanin, Nebojša; Djuric, Marko; Vesić, Ana; Strumberger, Ivana; Marjanović, Marina

doi:10.1007/978-981-19-6634-7_35

Cited by 8 publications

(2 citation statements)

References 42 publications

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“…Similarly, Das, Mandal, and Basak developed a three-parallel layered decision-based approach, which attained 98.4% accuracy in distinguishing spam from ham [23]. Zivkov and others deployed the evolutionary multi-verse optimizer swarm intelligence approach before classifying the CSDMC2010 dataset with LR to effectively separate spam emails [24]. Another evolutionary technique was suggested in [25], which used the atomic orbital search approach along with LR and gradient descent to improve the rate of detecting spam e-mails.…”

Section: Related Workmentioning

confidence: 99%

Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

Indu,

Dimri

2024

JWE

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Spam e-mails are continuously increasing and are a serious threats to a network and its users. Several efficient methods are available regarding this context, but still, it is evolving randomly. Considering this, the proposed approach addresses the problem of spam detection by combining traditional content-matching criteria with the modified version of the binomial logistic algorithm. The work generates seven categories for content-matching, which begins from three basic categories, namely: special words, adult content, and specific symbols and digits. The remaining four categories are derived from various possible combinations of these basic categories. The words selected for each category are carefully curated based on the human psychology of action and reaction. Then, a weight is assigned to each of the categories to signify their importance and a threshold criterion is deployed before implementing the binomial logistic algorithm, which not only increases the efficiency of the proposed algorithm but also reduces the rate of misclassification. The proposed model is tested on six separate datasets of Enron Spam Corpus, where 98.31% and 92.575% are the maximum and minimum accuracies achieved, respectively, in spam e-mail classification. The AUC_ROC scores for the entire Spam Corpus range between 0.927 and 0.983. A comparison is also carried out between the proposed algorithm and the other methods of spam detection that have logistic regression. Finally, the suggested method can adequately handle a large sample size without compromising the efficacy, which is measured using accuracy, precision, recall, F-measure, and AUC_ROC score.

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Section: Related Workmentioning

confidence: 99%

Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

Indu,

Dimri

2024

JWE

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“…It models the probability that a data point belongs to the positive or negative class using the logistic function [36]. The model is trained using the likelihood function and fit using optimization methods such as gradient descent [37]. The LR is simple and easy to understand but can have problems with nonlinear data and can be sensitive to outliers [38].…”

Section: ) Logistic Regressionmentioning

confidence: 99%

Comparison of Predictive Machine Learning Models to Predict the Level of Adaptability of Students in Online Education

Iparraguirre-Villanueva¹,

Torres-Ceclén²,

Epifanía-Huerta³

et al. 2023

IJACSA

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With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), K-Nearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as accuracy, specificity, sensitivity, F1 count and precision, have been used to evaluate the performance of each model. The results have shown that all five models can provide optimal results in terms of prediction. For example, the RF and XGB models presented the best performance with an accuracy rate of 92%, outperforming the other models. In consequence, it is suggested to use these two models RF and XGB for prediction of students' adaptability level in online education due to their higher prediction efficiency. Also, KNN, SVM and LR models, achieved a performance of 85%, 76%, 67%, respectively. In conclusion, the results show that the RF and XGB models have a clear advantage in achieving higher prediction accuracy. These results are in line with other similar works that used ML techniques to predict adaptability levels.

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