A. Marakhtanov, Evgeny O. Parenchenkov, N. Smirnov
{"title":"虚假注册账户的侦查","authors":"A. Marakhtanov, Evgeny O. Parenchenkov, N. Smirnov","doi":"10.1109/RusAutoCon52004.2021.9537341","DOIUrl":null,"url":null,"abstract":"This paper deals with the task of classification of accounts registered in e-commerce systems. The authors consider solving this problem with different machine learning algorithms and LSTM recurrent neural network. A brief description of machine learning algorithms and LSTM neural network used during the research is given in the paper. The dataset and algorithm of its preprocessing are described, additional features are introduced. The paper presents the results of numerical experiments: confusion matrices and classification metrics received with considered algorithms. Comparing of the models has been conducted on the basis of received metrics. The algorithm that provides the best metrics is selected. The SMOTE and ADASYN resampling algorithms are applied to the dataset, the received classification metrics are provided. The methods of improving classification results are proposed.","PeriodicalId":106150,"journal":{"name":"2021 International Russian Automation Conference (RusAutoCon)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of Fictitious Accounts Registration\",\"authors\":\"A. Marakhtanov, Evgeny O. Parenchenkov, N. Smirnov\",\"doi\":\"10.1109/RusAutoCon52004.2021.9537341\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper deals with the task of classification of accounts registered in e-commerce systems. The authors consider solving this problem with different machine learning algorithms and LSTM recurrent neural network. A brief description of machine learning algorithms and LSTM neural network used during the research is given in the paper. The dataset and algorithm of its preprocessing are described, additional features are introduced. The paper presents the results of numerical experiments: confusion matrices and classification metrics received with considered algorithms. Comparing of the models has been conducted on the basis of received metrics. The algorithm that provides the best metrics is selected. The SMOTE and ADASYN resampling algorithms are applied to the dataset, the received classification metrics are provided. The methods of improving classification results are proposed.\",\"PeriodicalId\":106150,\"journal\":{\"name\":\"2021 International Russian Automation Conference (RusAutoCon)\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Russian Automation Conference (RusAutoCon)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RusAutoCon52004.2021.9537341\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Russian Automation Conference (RusAutoCon)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RusAutoCon52004.2021.9537341","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper deals with the task of classification of accounts registered in e-commerce systems. The authors consider solving this problem with different machine learning algorithms and LSTM recurrent neural network. A brief description of machine learning algorithms and LSTM neural network used during the research is given in the paper. The dataset and algorithm of its preprocessing are described, additional features are introduced. The paper presents the results of numerical experiments: confusion matrices and classification metrics received with considered algorithms. Comparing of the models has been conducted on the basis of received metrics. The algorithm that provides the best metrics is selected. The SMOTE and ADASYN resampling algorithms are applied to the dataset, the received classification metrics are provided. The methods of improving classification results are proposed.