Extreme Learning machines and Support Vector Machines models for email spam detection

机译：用于电子邮件垃圾邮件检测的Extreme Learning机器和Support Vector Machines模型

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Extreme Learning machines (ELM) and Support Vector Machines have become two of the most widely used machine learning techniques for both classification and regression problems of recent. However the comparison of both ELM and SVM for classification and regression problems has often caught the attention of several researchers. In this work, an attempt has been made at investigating how SVM and ELM compared on the unique and important problem of Email spam detection, which is a classification problem. The importance of email in this present age cannot be overemphasized. Hence the need to promptly and accurately detect and isolate unsolicited mails through spam detection system cannot be over emphasized. Empirical results from experiments carried out using very popular dataset indicated that both techniques outperformed the best earlier published techniques on the same popular dataset employed in this study. However, SVM performed better than ELM on comparison scale based on accuracy. But in term of speed of operation, ELM outperformed SVM significantly.

机译：对于最近的分类和回归问题，极限学习机（ELM）和支持向量机已经成为使用最广泛的两种机器学习技术。但是，将ELM和SVM进行分类和回归问题的比较通常引起了一些研究人员的注意。在这项工作中，已经尝试调查SVM和ELM如何比较电子邮件垃圾邮件检测这一独特且重要的问题，这是一个分类问题。在当今这个时代，电子邮件的重要性不可过分强调。因此，过分强调通过垃圾邮件检测系统迅速准确地检测和隔离未经请求的邮件的需求。使用非常流行的数据集进行的实验的经验结果表明，在本研究中使用的同一流行数据集上，这两种技术均优于最佳的早期出版技术。但是，基于准确性，SVM在比较规模上的表现优于ELM。但是就操作速度而言，ELM明显优于SVM。

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《IEEE Canadian Conference on Electrical and Computer Engineering》|2017年|1-6|共6页
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Sunday Olusanya Olatunji;
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Support vector machines; Electronic mail; Testing; Neurons; Computers; Conferences; Classification algorithms;

机译：支持向量机;电子邮件;测试;神经元;计算机;会议;分类算法;

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1. Learning control of nonhonolomic robot based on support vector machine [J] . FENG Yong, GE Yun-jian, CAO Hui-bin, 中南大学学报（英文版） . 2012,第012期
2. A Hierarchical Clustering and Fixed-Layer Local Learning Based Support Vector Machine Algorithm for Large Scale Classification Problems [J] . WU Guang-chao, XIAO Fa-zhen, XI Jian-qing, 东华大学学报（英文版） . 2012,第001期
3. Incremental support vector machine algorithm based on multi-kernel learning [J] . Zhi yu Li, Jun feng Zhang, Shou song Hu 系统工程与电子技术（英文版） . 2011,第004期
4. Improved email spam detection model based on support vector machines [J] . Olatunji Sunday Olusanya Neural computing & applications . 2019,第3期

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5. A feature-centric spam email detection model using diverse supervised machine learning algorithms [J] . Ammara Zamir, Hikmat Ullah Khan, Waqar Mehmood, The Electronic Library . 2020,第3期

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6. Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system [J] . Al-Yaseen Wathiq Laftah, Othman Zulaiha Ali, Nazri Mohd Zakree Ahmad Expert Systems with Application . 2017,第jana期

机译：基于改进的K均值的入侵检测系统多级混合支持向量机和极限学习机
7. Extreme Learning machines and Support Vector Machines models for email spam detection [C] . Sunday Olusanya Olatunji IEEE Canadian Conference on Electrical and Computer Engineering . 2017

机译：极端学习机器和支持向量机模型用于电子邮件垃圾邮件垃圾邮件检测
8. On email spam filtering using support vector machine. [D] . Amayri, Ola. 2009

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Extreme Learning machines and Support Vector Machines models for email spam detection

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