Improved performance of fake account classifiers with percentage overlap features selection

Aris Tjahyanto, Rivanda Putra Pratama, A. M. Shiddiqi
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Abstract

Feature selection plays a crucial role in the development of high-performance classification models. We propose an innovative method for detecting fake accounts. This method leverages the percentage overlap technique to refine feature selection. We introduce our technique upon earlier work that showcased the enhanced efficacy of the Naïve Bayesian classifier through dataset normalization. Our study employs a dataset of account profiles sourced from Twitter, which we normalize using the Min-Max method. We analyze the results through a series of comprehensive experiments involving diverse classification algorithms—such as Naïve Bayes, decision tree, k-nearest neighbors (KNN), deep learning, and support vector machines (SVM). Our experimental results demonstrate a 100% accuracy achieved by the SVM and deep learning classifiers. The results are attributed to the percentage overlap technique, which facilitates the identification of four highly informative features. These findings outperform models with more extensive feature sets, underscoring the efficacy of our approach.
利用百分比重叠特征选择提高假账户分类器的性能
在开发高性能分类模型的过程中,特征选择起着至关重要的作用。我们提出了一种检测假账户的创新方法。该方法利用百分比重叠技术来完善特征选择。我们的技术是在早期工作的基础上提出的,早期工作展示了通过数据集规范化提高奈夫贝叶斯分类器的功效。我们的研究采用了来自 Twitter 的账户配置文件数据集,并使用 Min-Max 方法对其进行归一化处理。我们通过一系列综合实验对结果进行了分析,这些实验涉及多种分类算法,如奈夫贝叶斯、决策树、k-近邻(KNN)、深度学习和支持向量机(SVM)。实验结果表明,SVM 和深度学习分类器的准确率达到了 100%。这些结果归功于百分比重叠技术,该技术有助于识别四个高信息量特征。这些结果优于具有更广泛特征集的模型,凸显了我们方法的功效。
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