An Intelligent Hybrid Technique of Decision Tree and Genetic Algorithm for E-Mail Spam Detection

Taloba, Ahmed I.; Ismail, Safaa S. I.

doi:10.1109/icicis46948.2019.9014756

Cited by 19 publications

(12 citation statements)

References 5 publications

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“…Precision is measured by the amount of positive class predictions which belongs to the positive class [ 28 – 30 ]. Precision is characterized as the proportion of the rate of correctly classified events in all detected events.…”

Section: Resultsmentioning

confidence: 99%

Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network

Chawla

Balaji²,

Alabdali

et al. 2022

Journal of Healthcare Engineering

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A variety of receptor and donor characteristics influence long-and short-term kidney graft survival. It is critical to predict the effectiveness of kidney transplantation to optimise organ allocation. This would allow patients to choose the best accessible kidney donor and the optimal immunosuppressive medication. Several studies have attempted to identify factors that predispose to graft rejection, but the results have been contradictory. As a result, the goal of this paper is to use the African buffalo-based artificial neural network (AB-ANN) approach to uncover predictive risk variables related to kidney graft. These two feature selection approaches combine to provide a novel hybrid feature selection technique that could select the most important elements to improve prediction accuracy. The feature analysis revealed that clinical features have varied effects on transplant survival. The collected data is processed in both training and testing methods. The prediction model's performance, in terms of accuracy, precision, recall, and F-measure, was examined, and the results were compared with those of other existing systems, including naive Bayesian, random forest, and J48 classifier. The results suggest that the proposed approach can forecast graft survival in kidney recipients' next visits in a creative manner and with more accuracy compared with other classifiers. This proposed method is more efficient for predicting kidney graft survival. Incorporating those clinical tools into outpatient clinics’ everyday workflows could help physicians make better and more personalised decisions.

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Section: Resultsmentioning

confidence: 99%

Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network

Chawla

Balaji²,

Alabdali

et al. 2022

Journal of Healthcare Engineering

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“…Many researchers have also researched the human evolutionary processes to optimize the ML algorithm's performance. Taloba et al [8] explored Genetic Algorithm (GA) optimization by integrating it with Decision Tree (DT). The authors recognise the overfitting problem with dimension of feature space and attempt to overcome this issue by feature extraction with Principle Component Analysis (PCA).…”

Section: B Bio-inspired Methodsmentioning

confidence: 99%

“…where P is the probability and i is a node from the training data within both equation 7and (8). For the node: (c,C) // Use equation 7or (8) return Predicted Class (ĉ) Calculate the Accuracy;…”

Section: Decision Tree Classifiermentioning

confidence: 99%

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Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

et al. 2020

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Electronic mail has eased communication methods for many organisations as well as individuals. This method is exploited for fraudulent gain by spammers through sending unsolicited emails. This paper aims to present a method for detection of spam emails with machine learning algorithms that are optimized with bio-inspired methods. A literature review is carried to explore the efficient methods applied on different datasets to achieve good results. An extensive research was done to implement machine learning models using Naïve Bayes, Support Vector Machine, Random Forest, Decision Tree and Multi-Layer Perceptron on seven different email datasets, along with feature extraction and pre-processing. The bio-inspired algorithms like Particle Swarm Optimization and Genetic Algorithm were implemented to optimize the performance of classifiers. Multinomial Naïve Bayes with Genetic Algorithm performed the best overall. The comparison of our results with other machine learning and bio-inspired models to show the best suitable model is also discussed.

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“…Taloba and Ismail [77] developed a new machine learning approach for the hybrid decision tree and a genetic algorithm known as GADT for spam detection. The most significant algorithm for enhancing decision tree efficiency is the genetic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification Based on Decision Tree Algorithm for Machine Learning

Charbuty

Abdulazeez

2021

JASTT

707

211

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Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed.

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An Intelligent Hybrid Technique of Decision Tree and Genetic Algorithm for E-Mail Spam Detection

Cited by 19 publications

References 5 publications

Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network

Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network

Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

Classification Based on Decision Tree Algorithm for Machine Learning

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