恶意链接检测中的机器学习模型叠加

Yevhenii Khukalenko, Iryna Stopochkina, Mykola Ilin
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 An analysis of the performance of various classifiers on address and network groups of features was performed. A new classification model is proposed, which is a stacking of 3 models: kNN, XGBoost and Transformer. The best model for stacking was experimentally determined: Logistic Regression, which made it possible to improve the result of the best available model by 3%. The hypothesis that stacking a larger number of worse models has an advantage over stacking a smaller number of more productive models on the used data set was confirmed: regardless of the choice of stacking meta-algorithm, stacking of three models showed better results than stacking two.
 
 
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 An analysis of the performance of various classifiers on address and network groups of features was performed. A new classification model is proposed, which is a stacking of 3 models: kNN, XGBoost and Transformer. The best model for stacking was experimentally determined: Logistic Regression, which made it possible to improve the result of the best available model by 3%. The hypothesis that stacking a larger number of worse models has an advantage over stacking a smaller number of more productive models on the used data set was confirmed: regardless of the choice of stacking meta-algorithm, stacking of three models showed better results than stacking two.
 
 
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引用次数: 0

摘要

& # x0D;& # x0D;& # x0D;分析了各种分类器在地址组和网络组特征上的性能。提出了一种新的分类模型,该模型是kNN、XGBoost和Transformer 3个模型的叠加。通过实验确定了最佳的堆叠模型:Logistic回归,使最佳可用模型的结果提高了3%。证实了在使用的数据集上,将数量较多的较差模型叠加比将数量较少的较有效模型叠加更有优势的假设:无论选择哪种叠加元算法,三个模型叠加的效果都比两个模型叠加的效果好。 & # x0D;& # x0D;
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Machine Learning Models Stacking in the Malicious Links Detecting
An analysis of the performance of various classifiers on address and network groups of features was performed. A new classification model is proposed, which is a stacking of 3 models: kNN, XGBoost and Transformer. The best model for stacking was experimentally determined: Logistic Regression, which made it possible to improve the result of the best available model by 3%. The hypothesis that stacking a larger number of worse models has an advantage over stacking a smaller number of more productive models on the used data set was confirmed: regardless of the choice of stacking meta-algorithm, stacking of three models showed better results than stacking two.
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