图 书 馆
使用GNUsmail比较数据流挖掘的方法进行在线电子邮件分类Using GNUsmail to Compare Data Stream Mining Methods for On-line Email Classification |
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| 课程网址: | http://videolectures.net/wapa2011_baena_garcia_gnusmail/ |
| 主讲教师: | Manuel Baena-Garcia |
| 开课单位: | 马拉加大学 |
| 开课时间: | 2011-11-11 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Real-time classification of emails is a challenging task because of its online nature, and also because email streams are subject to concept drift. Identifying email spam, where only two different labels or classes are defined (spam or not spam), has received great attention in the literature. We are nevertheless interested in a more specific classification where multiple folders exist, which is an additional source of complexity: the class can have a very large number of different values. Moreover, neither cross-validation nor other sampling procedures are suitable for evaluation in data stream contexts, which is why other metrics, like the prequential error, have been proposed. However, the prequential error poses some problems, which can be alleviated by using recently proposed mechanisms such as fading factors. In this paper, we present GNUsmail, an open-source extensible framework for email classification, and we focus on its ability to perform online evaluation. GNUsmails architecture supports incremental and online learning, and it can be used to compare different data stream mining methods, using state-of-art online evaluation metrics. Besides describing the framework, characterized by two overlapping phases, we show how it can be used to compare different algorithms in order to find the most appropriate one. The GNUsmail source code includes a tool for launching replicable experiments. |
| 关 键 词: | 实时分类电子邮件; 数据流挖掘; 在线评价指标 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-05：lxf |
| 阅读次数: | 61 |