Michal Prilepok, T. Ježowicz, J. Platoš, V. Snás̃el
{"title":"使用压缩和PSO的垃圾邮件检测","authors":"Michal Prilepok, T. Ježowicz, J. Platoš, V. Snás̃el","doi":"10.1109/CASoN.2012.6412413","DOIUrl":null,"url":null,"abstract":"The problem of spam emails is still growing. Therefore, developing of algorithms which are able to solve this problem is also very active area. This paper presents two different algorithms for spam detection. The first algorithm is based on Bayesian filter, but it is improved using data compression algorithms in case that the Bayesian filter cannot decide. The second algorithm is based on document classification algorithm using Particle Swarm Optimization. Results of presented algorithms are promising.","PeriodicalId":431370,"journal":{"name":"2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Spam detection using compression and PSO\",\"authors\":\"Michal Prilepok, T. Ježowicz, J. Platoš, V. Snás̃el\",\"doi\":\"10.1109/CASoN.2012.6412413\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of spam emails is still growing. Therefore, developing of algorithms which are able to solve this problem is also very active area. This paper presents two different algorithms for spam detection. The first algorithm is based on Bayesian filter, but it is improved using data compression algorithms in case that the Bayesian filter cannot decide. The second algorithm is based on document classification algorithm using Particle Swarm Optimization. Results of presented algorithms are promising.\",\"PeriodicalId\":431370,\"journal\":{\"name\":\"2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CASoN.2012.6412413\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CASoN.2012.6412413","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The problem of spam emails is still growing. Therefore, developing of algorithms which are able to solve this problem is also very active area. This paper presents two different algorithms for spam detection. The first algorithm is based on Bayesian filter, but it is improved using data compression algorithms in case that the Bayesian filter cannot decide. The second algorithm is based on document classification algorithm using Particle Swarm Optimization. Results of presented algorithms are promising.
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